Sequencing Adapter Manufacture and Use

ABSTRACT

Technology provided herein relates in part to methods, processes, machines and apparatuses for determining sequences of nucleotides for nucleic acid templates in a nucleic acid sample. The technology provide herein also relates in part to methods, processes, machines and apparatuses for counting nucleic acid templates. Nucleic acid templates of a sample are tagged with nonrandom oligonucleotide adapters that include predetermined non-randomly generated sequences. The use of these nonrandom oligonucleotide adapters provides an efficient method to reduce sequencing errors, and increase the sensitivity of detection of low-frequency single nucleotide alterations.

RELATED PATENT APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/479,473, filed Jul. 19, 2019, which claims the benefit of U.S.Provisional Patent Application No. 62/448,601, filed on Jan. 20, 2017.The entire contents of the foregoing patent applications areincorporated herein in their entirety for all purposes.

FIELD

Technology provided herein relates in part to methods, processes,machines and apparatuses for determining sequences of nucleotides fornucleic acid templates in a nucleic acid sample. The technology provideherein also relates in part to methods, processes, machines andapparatuses for counting nucleic acid templates. Nucleic acid templatesof a sample are tagged with nonrandom oligonucleotide adapters thatinclude predetermined non-randomly generated molecular barcodesequences. The use of these nonrandom oligonucleotide adapters providesan efficient method to reduce sequencing errors, and increase thesensitivity of detection of low-frequency single nucleotide alterations.

BACKGROUND

Genetic information of living organisms (e.g., animals, plants andmicroorganisms) and other forms of replicating genetic information(e.g., viruses) is encoded in deoxyribonucleic acid (DNA) or ribonucleicacid (RNA). Genetic information is a succession of nucleotides ormodified nucleotides representing the primary structure of chemical orhypothetical nucleic acids. In humans, the complete genome containsabout 30,000 genes located on 24 chromosomes (i.e., 22 autosomes, an Xchromosome and a Y chromosome; see The Human Genome, T. Strachan, BIOSScientific Publishers, 1992). Each gene encodes a specific protein,which after expression via transcription and translation fulfills aspecific biochemical function within a living cell.

Many medical conditions are caused by one or more genetic variationsand/or genetic alterations. Certain genetic variations and/or geneticalterations cause medical conditions that include, for example,hemophilia, thalassemia, Duchenne Muscular Dystrophy (DMD), Huntington'sDisease (HD), Alzheimer's Disease and Cystic Fibrosis (CF) (Human GenomeMutations, D. N. Cooper and M. Krawczak, BIOS Publishers, 1993). Suchgenetic diseases can result from an addition, substitution, or deletionof a single nucleotide in DNA of a particular gene. Certain birthdefects are caused by a chromosomal abnormality, also referred to as ananeuploidy, such as Trisomy 21 (Down's Syndrome), Trisomy 13 (PatauSyndrome), Trisomy 18 (Edward's Syndrome), Monosomy X (Turner'sSyndrome) and certain sex chromosome aneuploidies such as Klinefelter'sSyndrome (XXY), for example. Another genetic variation is fetal gender,which can often be determined based on sex chromosomes X and Y. Somegenetic variations may predispose an individual to, or cause, any of anumber of diseases such as, for example, diabetes, arteriosclerosis,obesity, various autoimmune diseases and cancer (e.g., colorectal,breast, ovarian, lung, bladder, stomach, cervix, kidney, prostate,brain, and oesophageal).

Identifying one or more genetic variations and/or genetic alterations(e.g., copy number alterations, copy number variations, singlenucleotide alterations, single nucleotide variations, chromosomealterations, translocations, deletions, insertions, and the like) orvariances can lead to diagnosis of, or determining predisposition to, aparticular medical condition. Identifying a genetic variance can resultin facilitating a medical decision and/or employing a helpful medicalprocedure. In certain embodiments, identification of one or more geneticvariations and/or genetic alterations involves the analysis ofcirculating cell-free nucleic acid. Circulating cell-free nucleic acid(CCF-NA), such as cell-free DNA (CCF-DNA) for example, is composed ofDNA fragments that originate from cell death and circulate in peripheralblood. High concentrations of CF-DNA can be indicative of certainclinical conditions such as cancer, trauma, burns, myocardialinfarction, stroke, sepsis, infection, and other illnesses.Additionally, cell-free fetal DNA (CFF-DNA) can be detected in thematernal bloodstream and used for various noninvasive prenataldiagnostics.

SUMMARY

Provided herein in certain embodiments are compositions, methods andsystems for determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample.

In certain embodiments, this disclosure provides for a method fordetermining a sequence of nucleotides for one or more nucleic acidtemplates in a nucleic acid sample, comprising: contactingdouble-stranded nucleic acid templates of the nucleic acid sample withpartially double-stranded nonrandom oligonucleotide adapter speciesunder ligation conditions, thereby generating adapter-ligated nucleicacid templates, wherein: each of the nonrandom oligonucleotide adapterspecies comprises a first oligonucleotide species and a secondoligonucleotide species; each of the first oligonucleotide speciescomprises 5′ to 3′ a polynucleotide A and a polynucleotide B species andeach of the second oligonucleotide species comprises 5′ to 3′ apolynucleotide B′ species and a polynucleotide A′; each of thepolynucleotide B species and the polynucleotide B′ species arepredetermined, are non-randomly generated, are the same length, and areabout 4 to about 20 consecutive nucleotides in length; there are 300 orfewer polynucleotide B species and each polynucleotide B′ species is areverse complement of a polynucleotide B species; polynucleotide A isnot a reverse complement of polynucleotide A′; the ratio of nucleic acidtemplates to polynucleotide B species is greater than 1,000 to 1; andthe polynucleotide B species are annealed to complementarypolynucleotide B′ species and polynucleotide A′ is not annealed topolynucleotide A; and amplifying the adapter-ligated nucleic acidtemplates, thereby generating amplicons; and sequencing all or a portionof each amplicon, thereby determining a sequence of nucleotides for theone or more nucleic acid templates in the nucleic acid sample.

In certain embodiments the composition may comprise a plurality ofdouble stranded nucleic acid adapters. The individual nonrandomoligonucleotide adapter may comprise a defined first oligonucleotidespecies and a defined second oligonucleotide species. In certainembodiments each of the first oligonucleotide species may comprises 5′to 3′ a polynucleotide A species and a polynucleotide B species wherethe polynucleotide A is a different sequence than the polynucleotide B.Also in an embodiment, each of the second oligonucleotide speciescomprises 5′ to 3′ a polynucleotide B′ species, and a polynucleotide A′,wherein the polynucleotide B′ species are the reverse complement of thepolynucleotide B species, but the polynucleotide A species are not thereverse complement of the polynucleotide A′ species. In certainembodiments, each of the polynucleotide B species and the polynucleotideB′ species are predetermined, non-randomly generated sequences. Also insome embodiments, the polynucleotide B species and the polynucleotide B′species are the same length as each other. In certain embodiments, eachof the polynucleotide B and polynucleotide B′ species are the samelength as other polynucleotide B and B′ polynucleotide species of theset. In certain embodiments, the polynucleotide B species and thepolynucleotide B′ specie are about 4 to about 20 consecutive nucleotidesin length. In certain embodiments, the polynucleotide A species and thepolynucleotide A′ specie are about 4 to about 20 consecutive nucleotidesin length. The plurality of adapters, in some embodiments, may comprise999 or fewer polynucleotide B species. Also, in an embodiment, theadapters are designed such that the polynucleotide B species arepositioned 3′ to the A species of a first strand, and the polynucleotideB′ species are positioned 5′ to the polynucleotide A′ species of thesecond strand such that upon annealing, double-stranded adapters havinga “Y shape” with annealed polynucleotide B and polynucleotide B′sequences, and non-annealed polynucleotide A and polynucleotide A′sequences are formed.

In some embodiments the method may comprise contacting double-strandednucleic acid templates of the nucleic acid sample with partiallydouble-stranded nonrandom oligonucleotide adapter species as providedherein under ligation conditions, thereby generating adapter-ligatednucleic acid templates. The method may further comprise having thecontacting be under conditions such that the ratio of nucleic acidtemplates to polynucleotide B species is greater than 1,000 to 1. Themethod may further comprise having polynucleotide B and B′ species bethe reverse complement of each other, but the polynucleotide A speciesdesigned such that it is not a reverse complement of polynucleotide A′;such that upon annealing the polynucleotide B species are annealed tocomplementary polynucleotide B′ species and polynucleotide A′ is notannealed to polynucleotide A. Thus, in an embodiment, the adapters aredesigned such that the B species are positioned 3′ to the A species of afirst strand, and the B′ species are positioned 5′ to the A species ofthe second (complementary strand) such that upon annealing,double-stranded adapters having a “Y shape” with annealed B and B′sequences, and non-annealed A and A′ sequences are formed and annealedto the double-stranded template to form a double-stranded DNA moleculehaving “Y shaped” adapters on each end. The method may also comprisesthe step of amplifying the adapter-ligated nucleic acid templates,thereby generating amplicons. Also, in certain embodiments the methodmay comprise the step of sequencing all or a portion of each amplicon,thereby determining a sequence of nucleotides for the one or morenucleic acid template in the nucleic acid sample.

Also provided in certain aspects are methods for manufacturing a set ofnonrandom nucleic acid sequencing adapters, for use in determining asequence of nucleotides for one or more nucleic acid templates in anucleic acid sample. The set of adapters may be designed to include 999or fewer unique adapters such that the adapter set may be used insequencing DNA from a subject, where the ratio of the nonrandom nucleicacid sequencing adapters to nucleic acid templates of the nucleic acidsample is greater than 50 to 1. The method may comprise the step ofproviding a set of first oligonucleotide species and a set of secondoligonucleotide species, where each of the first oligonucleotide speciescomprises 5′ to 3′ a polynucleotide A and a polynucleotide B species andeach of the second oligonucleotide species comprises 5′ to 3′ apolynucleotide B′ species and a polynucleotide A′. In an embodiment,each of the polynucleotide B species and the polynucleotide B′ speciesare a predetermined sequence and as such, are non-randomly generated.Also in certain embodiments each paired B and B′ species and are thereverse complement of each other. In certain embodiments, each of the Band B′ species are the same length as other B and B′ species of the set.In certain embodiments, the B and B′ species may range from about 4 toabout 20 consecutive nucleotides in length. In certain embodiments;there are 999 or fewer polynucleotide B species and each polynucleotideB′ species is a reverse complement of a polynucleotide B species; theratio of nucleic acid templates to polynucleotide B species is greaterthan 1,000 to 1. In other embodiments; there are 500, or 400, or 300 orfewer polynucleotide B species and each polynucleotide B′ species is areverse complement of a polynucleotide B species; the ratio of nucleicacid templates to polynucleotide B species is greater than 1,000 to 1.In an embodiment, the adapters are configured such that polynucleotide Ais not a reverse complement of polynucleotide A′. The method maycomprise the step of synthesizing each of the first oligonucleotidespecies and each of the second oligonucleotide species separately. Themethod may also comprise the step of contacting each firstoligonucleotide species with each second oligonucleotide species inseparate pairs comprising the reverse complement polynucleotide B′species under annealing conditions, thereby generating partiallydouble-stranded adapter species; where the polynucleotide B species areannealed to complementary polynucleotide B′ species and polynucleotideA′ is not annealed to polynucleotide A. Thus, in an embodiment, theadapters are designed such that the polynucleotide B species arepositioned 3′ to the polynucleotide A species of a first strand, and thepolynucleotide B′ species are positioned 5′ to the polynucleotide A′species of the second strand such that upon annealing, double-strandedadapters having a “Y shape” with annealed polynucleotide B andpolynucleotide B′ sequences, and non-annealed polynucleotide A andpolynucleotide A′ sequences are formed

Also provided in certain aspects are methods for counting nucleic acidtemplates for a nucleic acid sample, comprising contactingdouble-stranded nucleic acid templates of the nucleic acid sample withpartially double-stranded nonrandom oligonucleotide adapter species asdescribed herein under ligation conditions, thereby generatingadapter-ligated nucleic acid templates, and sequencing the templates. Insome embodiments, the adapter-ligated templates may be subjected toamplification prior to sequencing thereby generating amplicons; andidentifying a set of amplicon duplicates, where the amplicon duplicatescomprise amplified adapter-ligated nucleic acid templates comprising apolynucleotide B species at one end; and determining the number ofamplicon duplicates comprising the polynucleotide B species.

Also provided are systems, machines and computer program products thatcarry out processes, or parts of processes, described herein. Certainembodiments are described further in the following description,examples, claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate certain embodiments of the technology and arenot limiting. For clarity and ease of illustration, the drawings are notmade to scale and, in some instances, various aspects may be shownexaggerated or enlarged to facilitate an understanding of particularembodiments.

FIG. 1 shows an illustrative embodiment of a system in which certainembodiments of the technology may be implemented.

FIG. 2 shows an illustrative embodiment of a process described herein.

FIG. 3 shows an illustrative embodiment of a process described herein.

FIG. 4 shows an illustrative embodiment of a process described herein.

FIG. 5 shows an illustrative embodiment of a process described herein.

FIG. 6 shows an illustrative embodiment of a process described herein.

FIG. 7 shows an illustrative embodiment of a process described herein.

FIG. 8 shows an illustrative embodiment of a process described herein.

FIG. 9 shows an illustrative embodiment of a process described herein.

FIG. 10 shows an alterations trend over three sample collection timepoints for a subject.

FIG. 11 provides a schematic of an example of a nonrandomoligonucleotide adapter.

FIG. 12A provides a schematic of annealed nonrandom oligonucleotideadapters positioned adjacent to nucleic acid template DNA, wherenonrandom oligonucleotide adapters are ligated to both ends of thenucleic acid template.

FIG. 12B is a schematic of the nonrandom oligonucleotide adapters ofFIG. 12A, ligated to nucleic acid template DNA, and also depictsuniversal amplification primers annealed at each end of one of thestrands of the construct.

FIG. 12C provides a schematic of the template strand 1 libraryconstruct, obtained using the nonrandom oligonucleotide adapters/nucleicacid template construct and primers of the bottom strand of FIG. 12B.

FIG. 12D provides a schematic of the template strand 2 library constructobtained using the nonrandom oligonucleotide adapters/nucleic acidtemplate construct and primers of the top strand of FIG. 12B.

FIG. 13 shows and embodiment of the validation of adapter constructsusing library yield for a readout of process efficiency.

FIG. 14 shows an embodiment of confirmation of the efficiency ofutilizing nonrandom duplex Y adapter (FDA-Y) adapters during librarypreparation and the optimization of their concentration to maximize thetarget library to adapter dimer ratio.

FIG. 15 shows an embodiment of modeling the likelihood of identicallabeling given the likelihood of identical fragment patterns.

FIG. 16 shows an embodiment of a determination of the number of uniquetemplates indistinguishable from each other on the basis of labeling andfragmentation of simulated samples.

FIG. 17 shows an embodiment demonstrating that the library preparationprocess is consistent and reproducible over consecutive runs.

DETAILED DESCRIPTION

Provided in certain embodiments herein are methods and compositions fordetermining nucleotide sequences for a nucleic acid sample. The methodsand compositions herein may be utilized for a variety of nucleic acidtemplates including, for example, fragmented or cleaved nucleic acid,cellular nucleic acid, and/or cell-free nucleic acid.

Thus, disclosed are nucleotide adapters and methods of using and makingsuch adapters. Also disclosed are systems employing such adapters.

In certain embodiments, nucleic acid templates of a sample may be taggedwith oligonucleotide adapters that include predetermined non-randomlygenerated molecular barcode sequences (nonrandom oligonucleotideadapters). The nonrandom oligonucleotide adapters may be prepared usingpredetermined barcode sequences, thus eliminating degenerate barcodesynthesis and purification steps. Using nonrandom oligonucleotideadapters may allow for a more streamlined approach to automatedsequencing of nucleic acid templates, while obtaining a low error rate.

Also provided herein are methods for the use of nonrandomoligonucleotide adapters to reduce sequencing errors, and increase thesensitivity of detection of genetic alterations (e.g., low-frequencysingle nucleotide alterations). The methods provided herein allow forsequencing of nucleic acid templates using a relatively low number ofnonrandom oligonucleotide adapter sequences, or tags. Use of apredetermined discrete set of nonrandom oligonucleotide adapters canprovide an additional layer of quality control during sequence analysis.For example, sequences obtained from nucleic acid templates ligated tothe nonrandom oligonucleotide adapters described herein may be analyzedin part according to barcode sequences. Sequences obtained containing abarcode not matching a barcode in the predetermined set may be removedfrom analysis, for example, as being potential spurious sequencingartifacts. Thus, the use of a relatively small number of nonrandomoligonucleotide adapter species may allow for the efficient sequencingof nucleic acid templates for a sample, and provides additional qualitycontrol for the sequence analysis.

Also provided are systems, machines and computer program products that,in some embodiments, carry out methods or parts of methods describedherein.

Nucleic Acid Templates and Adapters

In some embodiments of the present application, methods and compositionsare provided to determine a partial or full sequence of a nucleic acidtemplate. In other embodiments, methods and compositions are provided tocount nucleic acid templates for a nucleic acid sample.

By “nucleic acid template” is meant a full length nucleic acid molecule,or a portion, or fragment, of a full length nucleic acid molecule, asprovided herein. The nucleic acid template may be obtained by, forexample, enzyme digestion, sonication, nebulization, biologicalfragmentation, degradation, apoptosis, necrosis, or physical shearing oflarger nucleic acid molecules for a sample, and for example, by methodsprovided herein. For some applications, the nucleic acid template may besubjected to end repair, and end modification, such as “A-tailing” bymethods provided herein. A nucleic acid template may be, for example, afragment of a full length chromosome. A nucleic acid template may, forexample, be an extracellular DNA or RNA molecule, or a fragment thereof,and may, for example, be about 25 to 1000, 50 to 500, 50 to 400, 50 to300, or 50 to 100 base pairs in length, or may be at least 25, 30, 35,40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 120, 140, 160, 180,200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460,480, or 500 base pairs in length. Nucleic acid templates are generallydouble stranded, for most or all of the length, and may be referred toas template strand 1 and template strand 2. Template strand 1 andtemplate strand 2 generally are complementary to each other. In certaininstances, strands in a double stranded nucleic acid molecule may bereferred to as a first strand and a second strand, a leading strand anda lagging strand, a forward strand and a reverse strand, a plus strandand a minus strand, or a sense strand and an antisense strand. In someexamples, the nucleic acid templates are single stranded, and it isunderstood that the possible lengths provided herein may refer to bases,rather than base pairs. Where the template nucleic acid is singlestranded, a complementary strand may be generated, for example bypolymerization and/or reverse transcription, rendering the templatenucleic acid double stranded and having a first strand (i.e., templatestrand 1) and a second strand (i.e., template strand 2). In someembodiments, the nucleic acid templates for a nucleic acid samplecomprise cell-free DNA obtained following apoptosis. In someembodiments, the nucleic acid templates for a nucleic acid sample arefragmented or sheared molecules obtained from larger nucleic acidmolecules.

A nucleic acid template refers to a nucleic acid template molecule for asample. Each nucleic acid template for a sample is an individualmolecule having a full length nucleotide sequence. A full lengthsequence refers to the sequence of nucleotides spanning the entirelength of the individual template molecule (i.e., from the 5′ end to the3′ end). In certain embodiments, the molecule is double stranded, inother embodiments, the molecule is single stranded. In some embodiments,a nucleic acid template has the same full length nucleotide sequence asanother nucleic acid template for a sample. In some embodiments, anucleic acid template has a different, or unique full length nucleotidesequence as other nucleic acid templates for a sample. In someembodiments, a nucleic acid sample comprises at least 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500,600, 700, 800, 900, or 1000 nucleic acid templates having full lengthnucleotide sequences that differ from each other by at least onenucleotide. In some embodiments, a nucleic acid sample comprises atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90,100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 nucleic acidtemplate molecules having the same full length nucleotide sequence.Copies, or amplicons of an original sample nucleic acid templatemolecule generally comprise the same full length nucleotide sequence asthe original template molecule.

Provided in certain embodiments are methods of determining a sequence ofnucleotides for one or more nucleic acid templates in a nucleic acidsample. Such methods may comprise, for example, contactingdouble-stranded nucleic acid templates of a nucleic acid sample withpartially double-stranded nonrandom oligonucleotide adapter speciesunder ligation conditions, thereby generating nonrandom oligonucleotideadapter-ligated nucleic acid templates, where each of the nonrandomoligonucleotide adapter species comprises or comprises or consists of afirst oligonucleotide species and a second oligonucleotide species; eachof the first oligonucleotide species comprises 5′ to 3′ a polynucleotideA and a polynucleotide B species and each of the second oligonucleotidespecies comprises 5′ to 3′ a polynucleotide B′ species and apolynucleotide A′. In an embodiment, each of the polynucleotide Bspecies and the polynucleotide B′ species are predetermined, arenon-randomly generated, are the same length, and are about 4 to about 20consecutive nucleotides in length. Also, in an embodiment, there are 999or fewer, or 900 or fewer, or 800 or fewer, or 700 or fewer, or 600 orfewer, or 500 or fewer, or 400 or fewer, or 300 or fewer, or 200 orfewer, or 100 or fewer or about polynucleotide B species. In certainembodiments, the ratio of nucleic acid templates to polynucleotide Bspecies is greater than 1,000 to 1. Also in certain embodiments, eachpolynucleotide B′ species is a reverse complement of a polynucleotide Bspecies and polynucleotide A is not a reverse complement ofpolynucleotide A′, such that the polynucleotide B species are annealedto complementary polynucleotide B′ species and polynucleotide A′ is notannealed to polynucleotide A, such that the double-stranded adpaters are“Y-shaped” with the non-annealing A sequences at the end of the adapterthat is not ligated to the DNA template. The method may further compriseafter ligation of the adapters to the DNA template, amplifying thenonrandom oligonucleotide adapter-ligated nucleic acid templates,thereby generating amplicons; and sequencing all or a portion of eachamplicon, thereby determining a sequence of nucleotides for the one ormore nucleic acid templates in the nucleic acid sample.

As used herein, a polynucleotide B generally refers to a barcode, suchas, for example, a barcode described herein (e.g., a nonrandom molecularbarcode, a non-randomly generated molecular barcode, or a nondegenerateor non semidegenerate molecular barcode). A polynucleotide B speciesrefers to a polynucleotide having a nucleotide sequence that isdifferent from the nucleotide sequence of another polynucleotide Bspecies. Thus, the nucleotide sequence of B1 is distinct from B2, andB3-Bn, where n=the total number of B species. Similarly, the nucleotidesequence of B1′ (which is the reverse complement of B1) is distinct fromB2′, B3′-Bn′. A polynucleotide B species may be considered uniquecompared to other polynucleotide B species. By “different” or “unique”in this context is meant that when comparing one polynucleotide Bspecies with another polynucleotide B species, the two polynucleotide Bspecies have a nucleotide sequence that differs by at least onenucleotide identity. In some embodiments, polynucleotide B species in apredetermined set of polynucleotide B species differ from one another byat least 1 nucleotide identity. In some embodiments, polynucleotide Bspecies in a predetermined set of polynucleotide B species differ fromone another by at least 2 nucleotide identities. In some embodiments,polynucleotide B species in a predetermined set of polynucleotide Bspecies differ from one another by at least 3 nucleotide identities. Aset of polynucleotide B species that differ from one another by 1, 2, 3or more nucleotide identities may be referred to as a set of uniquepolynucleotide B species. A polynucleotide B species may refer to theindividual sequence of the polynucleotide B species before amplificationof the nonrandom oligonucleotide adapter ligated template. Followingamplification, there may be, for example, 10, 100, 1000, 10000, 100000,or more nucleic acid molecules, that is, copies of the nonrandomoligonucleotide adapter ligated templates, that have the same nucleotidesequence and therefore the same polynucleotide B species. An adapterspecies (e.g., double-stranded nonrandom oligonucleotide adapterspecies, partially double-stranded nonrandom oligonucleotide adapterspecies) generally comprises a polynucleotide B species and apolynucleotide B′ species, where the B′ species is the reversecomplement of the B species. Amplified copies of each strand of thenonrandom oligonucleotide adapter portion of the nonrandomoligonucleotide adapter ligated template may comprise either a singlepolynucleotide B or a single polynucleotide B′ species. In onenon-limiting embodiment, 288 unique B (and B′) sequences are used, thusproviding the ability to generate 288*288 unique template-adapterconstructs In one non-limiting embodiment, 1×10e11 to 2×10e11 moleculesof template nucleic acids are used in the ligation reaction in which thenumber of the adaptor molecules is 10-500 fold in excess of the templatenucleic acid molecules.

By nonrandom oligonucleotide adapter species is meant a nonrandomoligonucleotide adapter molecule that differs from another nonrandomoligonucleotide adapter molecule by at least one, at least two, or atleast three nucleotide identities. A nonrandom oligonucleotide adapterspecies typically refers to a nonrandom oligonucleotide adaptercomprising a unique polynucleotide B species.

Whereas two nonrandom oligonucleotide adapter species consist ofnucleotide sequences that differ by at least one nucleotide, two nucleicacid template molecules may, or may not, have the same nucleotidesequence. For purposes of further explication only, where a ligationreaction comprises a sample having 10,0000 nucleic acid templatemolecules and 20 nonrandom oligonucleotide adapter species, the 10,0000nucleic acid templates may also be referred to as 10,0000 nucleic acidmolecules, and the 20 nonrandom oligonucleotide adapter species may bereferred to as 20 nucleotide sequences. Using the designation “T” foreach nucleic acid template molecule, the ligation reaction includes T1,T2, T3, T4, T5 . . . T10,0000 nucleic acid templates. In certainexamples, the full length nucleotide sequence of, for example, T1,differs by at least one nucleotide from the full length nucleotidesequences of all of the other nucleic acid templates (T2-T10,000) forthe sample. In other examples, the full length nucleotide sequence of,for example, T1 is the same as at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 1000, 5000, or10,000 other nucleic acid templates for the sample. Regardless ofwhether two template molecules happen to share identical sequences ornot, each template in a sample is considered as an individual molecule.

Each nonrandom oligonucleotide adapter species comprises a B species andthe complementary B′ species. Using the designation “B” for each Bspecies, the ligation reaction in this example includes nonrandomoligonucleotide adapter species comprising B1 (and B1′), B2 (and B2′) .. . B20 (and B20′) species. Because each B species (and B′ species)refers to a nucleotide sequence, each ligation reaction may comprisemany copies of the same B species. Thus, in the ligation reaction wherethe nonrandom oligonucleotide adapters are provided in stoichiometricexcess of the nucleic acid templates, the reaction may comprise 1,000nonrandom oligonucleotide adapters having the B1 and B1′ species, 1,000having the B2 and B2′ species, etc. . . . . Thus, the reaction in thisembodiment would include 1,000×20=20,000 nonrandom oligonucleotideadapter molecules, and 10,000 nucleic acid template molecules. Becausethe reaction includes multiple copies of each nonrandom oligonucleotideadapter species, the reaction comprises 20 nonrandom oligonucleotideadapter species, also referred to as 20 B species, and 10,000 nucleicacid template molecules.

Typically, adapter species are ligated to template molecules at random.In some instances, two different nucleic acid templates may be ligatedto adapters comprising the same B species. In these instances, theadapter-ligated nucleic acid templates may be considered unique withrespect to the combination of molecular barcode B (or B′) sequence andthe nucleic acid template sequence; or the combination of molecularbarcode B (or B′) sequence and the mapped genomic coordinates of thenucleic acid template sequence (as determined by mapping the templatenucleotide sequence, as described herein). In some embodiments,adapter-ligated nucleic acid templates may be considered unique withrespect to the combination of a first molecular barcode B (or B′)sequence, a second molecular barcode B (or B′) sequence, and the nucleicacid template sequence; or the combination of a first molecular barcodeB (or B′) sequence, a second molecular barcode B (or B′) sequence, andthe mapped genomic coordinates of the nucleic acid template sequence. Insome embodiments, all or substantially all of the adapter-ligatednucleic acid templates in a sample may be considered unique. In someembodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or99% of the adapter-ligated nucleic acid templates in a sample may beconsidered unique.

A double-stranded nonrandom oligonucleotide adapter species may, forexample, be in a Y-shape, or in approximate Y-shape, in thatpolynucleotide A and polynucleotide A′ provide the arms of the Y, andannealed polynucleotide B and polynucleotide B′ species form the base ofthe Y shape. These adapters are referred to as Y adapters.Polynucleotides A and A′ generally comprise sequencing adapterpolynucleotides (e.g., Illumina adapter sequences or other sequencingadapter sequences) and/or sequences for amplifying the template nucleicacid (e.g., universal sequences for amplification primer annealing),and/or other sequences as described herein.

In some embodiments, the adapter is a nonrandom oligonucleotide adapterand the nonrandom oligonucleotide adapter species may, for example, bein a hairpin-shape, or in approximate hairpin-shape, collectivelyreferred to as the hairpin adapter. The hairpin adapters may comprisepolynucleotide A and polynucleotide A′, which form the non-complementaryportion (also known as the loop of the hairpin adapter); andpolynucleotide B and polynucleotide B′, which are annealed to form thecomplementary region (also known as the duplex region or the stem of thehairpin adapter). Polynucleotides A and A′ are joined in one DNA strand.In some cases, polynucleotides A and A′ are adjacent to each other. Insome cases, polynucleotides A and A′ are separated by 1-50 nucleotides,e.g., 1-30, 1-15, 1-10, 2-5 nucleotides. Polynucleotides A and A′generally comprise sequencing adapter polynucleotides (e.g., Illuminaadapter sequences) and/or sequences for amplifying the template nucleicacid (e.g., universal sequences for amplification primer annealing),and/or other sequences as described herein.

In some embodiments, the polynucleotide B species and the polynucleotideB′ species are non-degenerate. A non-degenerate set of polynucleotidespecies comprises a set of polynucleotides where each polynucleotidesequence is different, but the set does not represent examples of allpotential nucleotide combinations. In some embodiments, thepolynucleotide B species and the polynucleotide B′ species arenon-semidegenerate, that is, the set of polynucleotides includespositions where all possible nucleotide combinations are included. Thatis, in the example where A, C, T, and G are the possible nucleotides, inthe set of polynucleotides, there are examples at one or more positions,of each of the possible nucleotides. Where a polynucleotide species isreferred to as “non-degenerate” it is understood that the polynucleotidespecies is part of a non-degenerate set of polynucleotide species.

In contrast, a set of degenerate polynucleotide species, for example, isa set of polynucleotide species where each nucleotide position may beany nucleotide, such as, for example, A, C, G, T, or U, or anynucleotide analog with base pairing properties. Degeneratepolynucleotide sets cover all possible nucleotide combinations at eachposition. For purposes of this example, where A, C, T, and G are theonly nucleotides considered for the set, a polynucleotide species thatis 10 nucleotides in length would include 4¹⁰ polynucleotides,representing examples of different polynucleotides at each position.Thus, where one degenerate polynucleotide adapter species is ligated toeach nucleic acid template, for example, the number of degeneratepolynucleotide species in a set of species for a 10 nucleotide nonrandommolecular barcode of an adapter would be 4¹⁰. For sequencing in methodswhere an adapter is ligated to each end of a nucleic acid template, thenumber of pairs of adapter species would be 4¹⁰×4¹⁰=420. Where apolynucleotide species is referred to as “degenerate” it is understoodthat the polynucleotide species is part of a degenerate set ofpolynucleotide species. In some examples, the degenerate polynucleotidespecies may be part of a degenerate set of polynucleotide species wherenot every possible degenerate polynucleotide has been physicallysynthesized during the course of the synthesis reaction.

Using the non-degenerate, nonrandom oligonucleotide adapters discussedherein, nucleic acid templates may be counted, and/or may be sequenced,following contacting nonrandom oligonucleotide adapters under ligationconditions. Ligation conditions may include nonrandom oligonucleotideadapter molecules provided in stoichiometric excess of nucleic acidtemplates, while including an excess of nucleic acid templates tononrandom oligonucleotide adapter species. In an embodiment, the Aspecies are the same for each of the adapters and the 288 species aredefined by the B species. In one example, a pool of nonrandomoligonucleotide adapters includes 288 species. That is, while there maybe thousands of nonrandom oligonucleotide adapter molecules in theligation reaction, there are only 288 possible sequences for thenonrandom oligonucleotide adapters. Thus, theoretically, if onenonrandom oligonucleotide adapter is ligated to each nucleic acidtemplate, one of 288 possible nonrandom oligonucleotide adapter specieswill ligate to each template molecule. For adapter-ligated nucleic acidtemplates comprising two nonrandom oligonucleotide adapter species(i.e., one adapter at each end of the template molecule), the number ofpossible paired combinations of nonrandom oligonucleotide adapterspecies would be 288×288=82,944.

As noted above, double-stranded nucleic acid templates may be providedin excess of the double-stranded nonrandom oligonucleotide adapterspecies. That is, the ratio of the nucleic acid templates to thenonrandom oligonucleotide adapter species provided in the ligationreaction may be, for example, greater than 10 to 1, greater than 100 to1, greater than 1,000 to 1, greater than 10,000 to 1, greater than100,000 to 1, greater than 500,000 to 1, greater than 600,000 to 1,greater than 700,000 to 1, greater than 800,000 to 1, greater than900,000 to 1, or greater than 1,000,000 to 1.

The concentration of the non-random oligonucleotide adapter used forligating the nucleic acid templates may vary, in some embodiments, it isbetween 20-600 nM, e.g., 50-500 nM, 50-400 nM, 70-200 nM, or about 100nM. The concentration of the nucleic acid templates used in the ligationmay also vary, in some embodiments, the nucleic acid templates are 20-40ng, e.g., 25-30 ng.

In some embodiments, each of the first oligonucleotide species comprisesa polynucleotide C species between the polynucleotide A and thepolynucleotide B species; each of the second oligonucleotide speciescomprises a polynucleotide C′ species between polynucleotide A′ and thepolynucleotide B′ species. In certain embodiments, each polynucleotideC′ species is the reverse complement of the polynucleotide C species,and the polynucleotide C species are able to anneal to the complementarypolynucleotide C′ species. In some embodiments, each of thepolynucleotide C species comprises or consists of the same nucleotidesequence. In some embodiments, the polynucleotide C species compriseuniversal sequences, i.e., common sequences shared by differentadapters, and the polynucleotide C′ species comprise universalsequences. In some embodiments, the polynucleotide C species and/or thepolynucleotide C′ species may comprise an identifier, such as, forexample, an index polynucleotide or a barcode polynucleotide. In someembodiments, each of polynucleotide C species comprises or consists ofat least two different nucleotide sequences. The polynucleotide C and/orC′ species may comprise, for example, an identifier (e.g., a tag, anindexing tag), a capture sequence, a label, an adapter, a restrictionenzyme site, a promoter, an enhancer, an origin of replication, a stemloop, a complimentary sequence (e.g., a primer binding site, anannealing site), a suitable integration site (e.g., a transposon, aviral integration site), a modified nucleotide, the like or combinationsthereof.

In some embodiments, the double-stranded nucleic acid templates aredouble-stranded DNA templates. In some embodiments, the double-strandednucleic acid templates are double-stranded RNA templates. In someembodiments, the oligonucleotide adapters are DNA adapters. In someembodiments, the adapters are RNA adapters. In some cases, the adapterscomprise both DNA and RNA.

In certain embodiments, amplifying the adapter-ligated nucleic acidtemplates generates double-stranded amplicons, and sequencing comprisessequencing all or a portion of each strand of the amplicons. In someembodiments, the adapter-ligated nucleic acid templates are amplified bya process comprising linear amplification. In some embodiments, theadapter-ligated nucleic acid templates are amplified by a processcomprising exponential amplification. In some embodiments, theadapter-ligated nucleic acid templates are amplified by a processcomprising isothermal amplification. In some embodiments, theadapter-ligated nucleic acid templates are amplified by a processcomprising a single primer extension reaction. In some embodiments,amplification is not performed prior to a clustering reaction on aninstrument capable of next generation sequencing.

In certain embodiments, the double-stranded nucleic acid templates areblunt-ended. In some embodiments, the nucleic acid templates comprise atleast one blunt end. In some embodiments, the nucleic acid templates aresheared double-stranded DNA templates. In some embodiments, the nucleicacid templates are restriction enzyme-digested double-stranded DNAtemplates. In some embodiments, the method comprises blunt-ending thenucleic acid templates before contacting the nucleic acid templates withthe nonrandom oligonucleotide adapter species. In some embodiments, thenonrandom oligonucleotide adapter species comprise a blunt end. In someembodiments, the double-stranded nucleic acid templates comprise aligation linker.

In some embodiments, the method comprises joining a ligation linker tothe blunt end of a nucleic acid template. In some embodiments, theligation linker is comprises at least one of an A-overhang, T-overhang,a CG-overhang, a blunt end, or any ligatable nucleic acid sequence. Insome embodiments, the ligation linker is an A-overhang. In someembodiments, the double-stranded nonrandom oligonucleotide adapterspecies comprises a ligation linker. In some embodiments, the ligationlinker is selected from the group consisting of an A-overhang,T-overhang, a CG-overhang, a blunt end, or any ligatable nucleic acidsequence. In some embodiments, the ligation linker is a T-overhang. Insome embodiments, both ends of a nucleic acid template include the sametype of ligation linker. In some embodiments, both ends of a nucleicacid template include different types of ligation linkers.

In certain embodiments, the nucleic acid sample is obtained from asubject. In some embodiments, the nucleic acid is cell-free nucleicacid. In some embodiments, the nucleic acid sample is blood plasma,blood serum, or urine. In some embodiments, the nucleic acid sample iscirculating cell-free nucleic acid. In some embodiments, the nucleicacid sample is isolated from blood plasma, blood serum, or urine. Insome embodiments, the nucleic acid sample is isolated from a sample oftissue, cells, or fluid obtained from a subject. In some embodiments,the subject is human.

In certain embodiments, the nucleic acid sample is isolated from asample of tissue, cells, or fluid obtained from a subject. In certainembodiments, the nucleic acid sample is partially purified from a sampleof tissue, cells, or fluid obtained from a subject. In some embodiments,the sequence of nucleotides for one or more nucleic acid templates in anucleic acid sample is determined in situ. In some embodiment, thenucleic acid templates are enriched. In some embodiments, the methodcomprises capturing a subset of the nucleic acid templates byhybridization to capture probes under hybridization conditions, therebygenerated captured target nucleic acid templates. In some embodiments,the method comprises: enriching for target nucleic acid templatesrepresenting one or more selected genes by means of amplifying targetnucleic acid templates in the nucleic acid sample that are complementaryto selected genes. In some embodiments, the method of obtaining thenucleic acid sample comprises eluting captured target nucleic acidtemplates from the capture probes. In some embodiments, the captureprobes are in an array. In some embodiments, the capture probes areattached to beads.

In certain embodiments of the method, the sequencing depth is at about100 fold to about 200,000 fold. In some embodiments, the sequencingdepth is at about 1,000 fold to about 150,000 fold. In some embodiments,the sequencing depth is at about 5,000 fold to about 100,000 fold. Insome embodiments, the sequencing depth is at about 10,000 fold to about70,000 fold. In some embodiments, the sequencing depth is at about20,000 fold to about 60,000 fold. In some embodiments, the sequencingdepth is at about 30,000 fold to about 50,000 fold. In some embodiments,the sequencing depth is about 100; 200; 300; 400; 500; 600; 700; 800;900; 1,000; 1,250; 1,500; 1,750 2,000; 2,250; 2,500, 2, 750; 3,000;3,500; 4,000; 4,500; 5,000; 5,500; 6,000; 6,500; 7,000; 7,500; 8,000;8,500; 9,000; 9,500; 10,000; 15,000; 20,000; 25,000; 30,000; 35,000;40,000; 45,000; 50,000; 55,000; 60,000; 65,000; 70,000; 75,000; 80,000;85,000; 90,000; 95,000; 100,000; 110,000; 120,000; 130,000; 140,000;150,000; 160,000; 170,000; 180,000; 190,000; or 200,000 fold.

Template Preparation for Sequencing

In certain embodiments, the disclosed sequencing adapters are ligated toeach end of a nucleic acid templates. Either one, or both of theadapters may comprise nonrandom molecular barcode sequences. Asequencing adapter that comprises a nonrandom molecular barcode sequencemay be referred to herein as a “nonrandom oligonucleotide adapter.”Provided in certain embodiments of the methods herein are sequencingmethods where each of the nucleic acid templates is ligated to onenonrandom oligonucleotide adapter. In certain embodiments, eachadapter-ligated nucleic acid template, having a first end and a secondend, comprises one nonrandom oligonucleotide adapter at one of the firstand second ends. In certain embodiments, each adapter-ligated nucleicacid template comprises a nonrandom oligonucleotide adapter at a firstend and a standard sequencing adapter at a second end. In certainembodiments, each adapter-ligated nucleic acid template comprises orconsists of one nonrandom oligonucleotide adapter at one end. In certainembodiments, each adapter-ligated nucleic acid template comprises orconsists of one nonrandom oligonucleotide adapter at a first end and astandard sequencing adapter at a second end.

By “standard sequencing adapter” in the context of adapters ligated tomore than one nucleic acid template is meant that the adapter providedin the ligation reaction for ligation to a sample of nucleic acidtemplates comprises or comprises or consists of the same nucleotidesequence. As used herein, a standard sequencing adapter generally doesnot comprise part of the nonrandom B and B′ species oligonucleotide ofthe disclosed adapters. Standard sequencing adapters may comprise, forexample, universal sequences, sample ID sequences, and the like, butwhere a collection of standard sequencing adapters are provided in aligation reaction, each standard sequencing adapter comprises orcomprises or consists of the same nucleotide sequence. For example, atleast 80, 85, 90, 95, 96, 97, 98, or 99% of the standard sequencingadapters ligated to nucleic acid templates in a sample of the presentapplication consist of the same nucleotide sequence. Standard sequencingadapters may include, for example, Illumina sequencing adapters for usewith systems such as, for example, MISEQ, NEXTSEQ, and HISEQ systems.

Provided in certain embodiments of the methods herein, are sequencingmethods where each nucleic acid template is ligated to two nonrandomoligonucleotide adapters (e.g., one adapter on each end of the templatemolecule). In certain embodiments, each adapter-ligated nucleic acidtemplate comprises a first nonrandom oligonucleotide adapter at a firstend and a second nonrandom oligonucleotide adapter at a second end. Thefirst and second nonrandom oligonucleotide adapters may or may notcomprise the same B-species. In some embodiments, the B species at eachend of the adapter-ligated nucleic templates are the same or different.Often, the B species at each end of the adapter-ligated nucleictemplates are different.

Nonrandom oligonucleotide adapters comprising a particular B species(e.g., “B1”) may be ligated to more than one nucleic acid template.Often, nonrandom oligonucleotide adapters comprising a particular Bspecies, e.g., B1, are ligated to more than one nucleic acid template,where each nucleic acid template has a different sequence (e.g., T1, T2,T3 . . . ). In such instances, ligation reactions may produce B1T1,B1T2, B1T3 nonrandom oligonucleotide adapter-ligated nucleic acidtemplates. In some embodiments, one of the B species for one nonrandomoligonucleotide adapter-ligated nucleic acid template is the same as oneof the B species for another nonrandom oligonucleotide adapter-ligatednucleic acid template. In some embodiments, both of the B species forone nonrandom oligonucleotide adapter-ligated nucleic acid template arethe same as both of the B species for another nonrandom oligonucleotideadapter-ligated nucleic acid template. In some embodiments, the Bspecies for at least two adapter-ligated nucleic acid templates consistof a different nucleotide sequence. In some embodiments, copies of afirst double-stranded adapter species comprising a first B species and afirst B′ species are ligated to at least two double-stranded nucleicacid templates.

In certain embodiments of the methods herein, copies of a firstdouble-stranded nonrandom oligonucleotide adapter species comprising afirst B species and a first B′ species (e.g., B1 and B1′) are ligated toa first end of at least two double-stranded nucleic acid templates; andcopies of a second double-stranded nonrandom oligonucleotide adapterspecies comprising a second B species and a second B′ species (e.g., B2and B2′) are ligated to a second end of at least two double-strandednucleic acid templates. In some embodiments, the at least twodouble-stranded nucleic acid templates comprise or consist of nucleotidesequences that differ by at least one nucleotide.

As noted above, the number of nonrandom oligonucleotide adapter DNAmolecules provided in the ligation reactions typically is in excess ofthe amount of nucleic acid template molecules. This excess of nonrandomoligonucleotide adapter DNA molecules helps to ensure that each nucleicacid template is ligated to oligonucleotide adapters. The ratio ofnonrandom oligonucleotide adapter molecules to nucleic acid templatesprovided in the ligation reaction can be, for example, greater than 20to 1. In some embodiments, the ratio of nonrandom oligonucleotideadapter molecules to nucleic acid templates is, for example, greaterthan 10 to 1, 15 to 1, 20 to 1, 25 to 1, 30 to 1, 35 to 1, 40 to 1, 45to 1, 50 to 1, 55 to 1, 60 to 1, 65 to 1, 70 to 1, 75 to 1, 80 to 1, 85to 1, 90 to 1, 95 to 1, or 100 to 1. In some embodiments, the ratio ofnonrandom oligonucleotide adapter molecules to nucleic acid templatesis, for example, less than 100 to 1, for example, less than 50 to 1, forexample, less than 45 to 1. In some embodiments, the ratio of nonrandomoligonucleotide adapter molecules to nucleic acid templates is about 20to 1, 30 to 1, 40 to 1, 45 to 1,50 to 1, 55 to 1,60 to 1, 65 to 1, or 70to 1.

In contrast to the ratio of molecules of nonrandom oligonucleotideadapter molecules to molecules of nucleic acid template, the number ofunique nucleic acid templates in the ligation reaction is provided inexcess of the number of unique nonrandom oligonucleotide adapterspecies, or nonrandom oligonucleotide adapter sequences, comprisingunique molecular barcodes. Multiple copies of a nonrandomoligonucleotide adapter species comprising a unique polynucleotide Bspecies sequence and the reverse complement polynucleotide B′ speciesmay be present in the ligation reaction. The number of nonrandomoligonucleotide adapter species is provided in a depleting amount, andas a result of the ligation reaction, each nonrandom oligonucleotideadapter-ligated nucleic acid template may, in some instances, notcomprise a unique polynucleotide B nucleotide sequence, or uniquemolecular barcode. Copies of a nonrandom oligonucleotide adaptercomprising the same B species or molecular barcode may be ligated tomore than one nucleic acid template. In some embodiments, the ratio ofthe number of nucleic acid templates to the number of nonrandomoligonucleotide adapter species (each comprising a different B speciesnucleotide sequence) is about 1,000,000 to 1. In some embodiments, theratio of nucleic acid templates to the number of nonrandomoligonucleotide adapter species is greater than 100 to 1, 250 to 1, 500to 1, 750 to 1, 1,000 to 1, 5,000 to 1, 10,000 to 1, 20,000 to 1, 30,000to 1, 40,000 to 1, 50,000 to 1, 60,000 to 1, 70,000 to 1, 80,000 to 1,90,000 to 1, 100,000 to 1, 200,000 to 1, 300,000 to 1, 400,000 to 1,500,000 to 1, 600,000 to 1, 700,000 to 1, 800,000 to 1, 900,000 to 1,1,000,000 to 1, 1,200,000 to 1, 1,400,000 to 1, 1,600,000 to 1,1,800,000 to 1, or 2,000,000 to 1.

Provided in some embodiments, in a nucleic acid template-nonrandomoligonucleotide adapter ligation reaction, there are 999 or fewerpolynucleotide B species. In some embodiments, there are 400 or fewerpolynucleotide B species. In some embodiments, there are 300 or fewerpolynucleotide B species. In some embodiments, there are about 300 toabout 400 polynucleotide B species. In some embodiments, there are about100 to about 500 polynucleotide B species. In some embodiments, thereare about 200 to 300 polynucleotide B species. In some embodiments,there are about 280 to about 290 polynucleotide B species In someembodiments, there are 288 B species. In some embodiments, there are1000, 750, 500, 475, 450, 425, 400, 375, 350, 325, 300, 275, 250, 225,200, 175, 150, 125, or 100 or fewer polynucleotide B species.

Thus, in a ligation reaction that comprises, for purposes of thisexample, 300,000 nucleic acid templates and 300 polynucleotide Bspecies, at a ratio of 1,000 nucleic acid templates per polynucleotide Bspecies, where the nonrandom oligonucleotide adapter molecules areprovided in excess (1,000 fold) of the nucleic acid templates, thereaction includes 300,000 nucleic acid templates and 3×10⁸ nonrandomoligonucleotide adapter molecules. The 3×10⁸ nonrandom oligonucleotideadapter molecules represent 300 B species, that is, 300 B sequences. Thereaction of this example includes 1,000 nonrandom oligonucleotideadapter molecules comprising the B1 sequence, 1,000 comprising the B2sequence, etc. . . . to the B300 sequence. And, the reaction of thisexample includes 300,000 nucleic templates (molecules): T1, T2, T3, T4 .. . T300,000. Nucleic acid templates may or may not have the samenucleotide sequence.

In some embodiments, there may be more than one nonrandomoligonucleotide adapter species that has the same polynucleotide Bspecies present in a ligation reaction. In embodiments, less than 90,80, 70, 60, 50, 40, 30, 20, 10, 5, 4, 3, 2, or 1% of the nonrandomoligonucleotide adapter-ligated nucleic acid templates comprise apolynucleotide B species that is different from the polynucleotide Bspecies on the other nonrandom oligonucleotide adapter-ligated nucleicacid templates.

As discussed in more detail herein, the use of the disclosed Y adapterscan provide for increased specificity and sensitivity in DNA sequencingreactions. The use of double-stranded Y adapters allows for the twostrands (i.e., forward and reverse) of each DNA template to beidentified as being from the same molecule. In this way, if there is adetected base change at a particular position in the DNA template on onestrand that is not replicated for the other strand, it can be inferredthat the base change is due to artifactual errors introduced duringsample processing including, but not limited to, sequencing errors, andis not a true mutation.

The use of nonrandom duplex adapters is more advantageous over thetraditional random duplex adaptors. First, manufacture of nonrandomduplex adapters is more direct and streamlined, as it does not requireextension and cleavage, but only hybridization of oligonucleotides.Second, through careful design of polynucleotide species, as opposed torandomization, sample processing errors occurring within thepolynucleotide motif can be corrected for and eliminated, which isexpected to improve the ultimate data quality. Third, nonrandom duplexadapters are significantly more efficient at sampling template moleculesthan are random duplex adapters; an illustrative examples is shown inFIG. 13 , in which nonrandom duplex adapters results in higher libraryyield than randon duplex adapters.

In some embodiments, the presence of a single nucleotide alteration inthe nucleic acid template is determined and the single nucleotidealteration is present at a frequency of 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5,or 1 percent or lower. In some embodiments, the single nucleotidealteration is present at a frequency lower than 1 percent. In someembodiments, the single nucleotide alteration is present at a frequencyof 1, 0.75, 0.5, 0.25, 0.1, 0.075, 0.05, 0.025, 0.01, 0.0075, 0.005,0.0025, 0.001, 0.00075, 0.0005, 0.00025, or 0.0001 percent or lower.

In some embodiments, the ratio of the number of nucleic acid templatesfor the nucleic acid sample to the number of polynucleotide B species inthe nonrandom oligonucleotide adapters is about 1,000,000 to 1, thepresence of a single nucleotide alteration in the nucleic acid templateis determined and the single nucleotide alteration is present at afrequency of 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5, or 1 percent or lower. Insome embodiments, the single nucleotide alteration is present at afrequency lower than 1 percent. In some embodiments, the singlenucleotide alteration is present at a frequency of 1, 0.75, 0.5, 0.25,0.1, 0.075, 0.05, 0.025, 0.01, 0.0075, 0.005, 0.0025, 0.001, 0.00075,0.0005, 0.00025, or 0.0001 percent or lower.

In some embodiments, the ratio of the number of nucleic acid templatesfor the nucleic acid sample to the number of polynucleotide B species inthe nonrandom oligonucleotide adapters is about 1,000,000; 900,000;800,000; 700,000; 600,000; 500,000; 400,000; 300,000; 200,000; 100,000;50,000; 25,000; or 10,000 to 1, the presence of a single nucleotidealteration in the nucleic acid template is determined and the singlenucleotide alteration is present at a frequency of 5, 4.5, 4, 3.5, 3,2.5, 2, 1.5, or 1 percent or lower. In some embodiments, the singlenucleotide alteration is present at a frequency lower than 1 percent. Insome embodiments, the single nucleotide alteration is present at afrequency of 1, 0.75, 0.5, 0.25, 0.1, 0.075, 0.05, 0.025, 0.01, 0.0075,0.005, 0.0025, 0.001, 0.00075, 0.0005, 0.00025, or 0.0001 percent orlower.

In certain embodiments, the method comprises providing a base call,where each base call represents a single nucleotide located at a singlenucleotide position in the nucleic acid template. In some embodiments,the frequency of base call errors is 0.9×10⁻³, 0.7×10⁻³, 0.5×10⁻³,0.25×10⁻³, 1×10⁻⁴, 7×10⁻⁵, 5×10⁻⁵, 3×10⁻⁵, 1×10⁻⁵, 7×10⁻⁶, 5×10⁻⁶,3×10⁻⁶, 1×10⁻⁶, 7×10⁻⁷, 5×10⁻⁷, 3×10⁻⁷ , or 1×10⁻⁷ or lower. In someembodiments, the frequency of base call errors is 1×10⁻³ or lower. Insome embodiments, the frequency of base call errors is lower than1×10⁻³. In some embodiments, the frequency of base call errors is0.9×10⁻³, 0.8×10⁻³, 0.7×10⁻³, 0.6×10⁻³, 0.5×10⁻³, 0.4×10⁻³, 0.3×10⁻³,0.2×10⁻³, 1×10⁻⁴, 0.9×10⁻⁴, 0.8×10⁻⁴, 0.7×10⁻⁴, 0.6×10⁻⁴, 0.5×10⁻⁴,0.4×10⁻⁴, 0.3×10⁻⁴, 0.2×10⁻⁴, 1×10⁻⁵, 0.9×10⁻⁵, 0.8×10⁻⁵, 0.7×10⁻⁵,0.6×10⁻⁵, 0.5×10⁻⁵, 0.4×10⁻⁵, 0.3×10⁻⁵, 0.2×10⁻⁵, 1×10⁻⁶, 0.9×10⁻⁶,0.8×10⁻⁶, 0.7×10⁻⁶, 0.6×10⁻⁶, 0.5×10⁻⁶, 0.4×10⁻⁶, 0.3×10⁻⁶, 0.2×10⁻⁶, or1×10⁻⁷ or lower.

The ratio of nonrandom oligonucleotide adapter molecules to nucleic acidtemplates provided in the ligation reaction is, for example, greaterthan 20 to 1. In some embodiments, the ratio of nonrandomoligonucleotide adapter molecules to nucleic acid templates is, forexample, greater than 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, or 100 to 1. In some embodiments, the ratio ofnonrandom oligonucleotide adapter molecules to nucleic acid templatesis, for example, less than 100 to 1, for example, less than 50 to 1, forexample, less than 45 to 1. In some embodiments, the ratio of nonrandomoligonucleotide adapter molecules to nucleic acid templates is about 20,30, 40, 45, 50, 55, 60, 65, or 70 to 1.

In some embodiments, the method comprises base calls, where each basecall represents a single nucleotide in the nucleic acid template; theratio of the number of nucleic acid templates for the nucleic acidsample to the number of polynucleotide B species in the nonrandomoligonucleotide adapters is about 1,000,000 to 1; and the frequency ofbase call errors is 1×10⁻⁷ or lower. In some embodiments, the methodcomprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1; and the frequency of base call errors is 1×10⁻⁶ orlower. In some embodiments, the method comprises base calls, where eachbase call represents a single nucleotide in the nucleic acid template;the ratio of the number of nucleic acid templates for the nucleic acidsample to the number of polynucleotide B species in the nonrandomoligonucleotide adapters is about 1,000,000 to 1; and the frequency ofbase call errors is 0.5×10⁻⁶ or lower. In some embodiments, the methodcomprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1; and the frequency of base call errors is 1×10⁻⁵ orlower. In some embodiments, the method comprises base calls, where eachbase call represents a single nucleotide in the nucleic acid template;the ratio of the number of nucleic acid templates for the nucleic acidsample to the number of polynucleotide B species in the nonrandomoligonucleotide adapters is about 1,000,000 to 1; and the frequency ofbase call errors is 0.8×10⁻⁵ or lower. In some embodiments, the methodcomprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1; and the frequency of base call errors is 0.5×10⁻⁵or lower. In some embodiments, the method comprises base calls, whereeach base call represents a single nucleotide in the nucleic acidtemplate; the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is about 1,000,000 to 1; and thefrequency of base call errors is 0.3×10⁻⁵ or lower. In some embodiments,the method comprises base calls, where each base call represents asingle nucleotide in the nucleic acid template; the ratio of the numberof nucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1; and the frequency of base call errors is 1×10⁻⁴ orlower. In some embodiments, the method comprises base calls, where eachbase call represents a single nucleotide in the nucleic acid template;the ratio of the number of nucleic acid templates for the nucleic acidsample to the number of polynucleotide B species in the nonrandomoligonucleotide adapters is about 1,000,000 to 1; and the frequency ofbase call errors is 0.8×10⁻⁴ or lower. In some embodiments, the methodcomprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1; and the frequency of base call errors is 0.5×10⁻⁴or lower. In some embodiments, the method comprises base calls, whereeach base call represents a single nucleotide in the nucleic acidtemplate; the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is about 1,000,000 to 1; and thefrequency of base call errors is 0.3×10⁻⁴ or lower. In some embodiments,the method comprises base calls, where each base call represents asingle nucleotide in the nucleic acid template; the ratio of the numberof nucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1; and the frequency of base call errors is lowerthan 1×10⁻³. In some embodiments, the method comprises base calls, whereeach base call represents a single nucleotide in the nucleic acidtemplate; the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is about 1,000,000 to 1; and thefrequency of base call errors is 1×10⁻³ or lower.

In some embodiments, the method comprises base calls, where each basecall represents a single nucleotide in the nucleic acid template; theratio of the number of nucleic acid templates to the number ofpolynucleotide B species (for example, the number of nonrandomoligonucleotide adapter species, each comprising a differentpolynucleotide B species) is greater than 1,000,000, 900,000, 800,000,700,000, 600,000, 500,000, 400,000, 300,000, 200,000, 100,000, 80,000,60,000, 40,000, 20,000, 10,000 to 1; and the frequency of base callerrors is 1×10⁻⁷ or lower. In some embodiments, the method comprisesbase calls, where each base call represents a single nucleotide in thenucleic acid template; the ratio of the number of nucleic acid templatesto the number of polynucleotide B species is greater than 1,000,000,900,000, 800,000, 700,000, 600,000, 500,000, 400,000, 300,000, 200,000,100,000, 80,000, 60,000, 40,000, 20,000, 10,000 to 1; and the frequencyof base call errors is 1×10⁻⁶ or lower. In some embodiments, the methodcomprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates to the number of polynucleotide B species isgreater than 1,000,000, 900,000, 800,000, 700,000, 600,000, 500,000,400,000, 300,000, 200,000, 100,000, 80,000, 60,000, 40,000, 20,000,10,000 to 1; and the frequency of base call errors is 0.5×10⁻⁶ or lower.In some embodiments, the method comprises base calls, where each basecall represents a single nucleotide in the nucleic acid template; theratio of the number of nucleic acid templates to the number ofpolynucleotide B species is greater than 1,000,000, 900,000, 800,000,700,000, 600,000, 500,000, 400,000, 300,000, 200,000, 100,000, 80,000,60,000, 40,000, 20,000, 10,000 to 1; and the frequency of base callerrors is 1×10⁻⁵ or lower. In some embodiments, the method comprisesbase calls, where each base call represents a single nucleotide in thenucleic acid template; the ratio of the number of nucleic acid templatesto the number of polynucleotide B species is greater than 1,000,000,900,000, 800,000, 700,000, 600,000, 500,000, 400,000, 300,000, 200,000,100,000, 80,000, 60,000, 40,000, 20,000, 10,000 to 1; and the frequencyof base call errors is 0.8×10⁻⁵ or lower. In some embodiments, themethod comprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates to the number of polynucleotide B species isgreater than 1,000,000, 900,000, 800,000, 700,000, 600,000, 500,000,400,000, 300,000, 200,000, 100,000, 80,000, 60,000, 40,000, 20,000,10,000 to 1; and the frequency of base call errors is 0.5×10⁻⁵ or lower.In some embodiments, the method comprises base calls, where each basecall represents a single nucleotide in the nucleic acid template; theratio of the number of nucleic acid templates to the number ofpolynucleotide B species is greater than 1,000,000, 900,000, 800,000,700,000, 600,000, 500,000, 400,000, 300,000, 200,000, 100,000, 80,000,60,000, 40,000, 20,000, 10,000 to 1; and the frequency of base callerrors is 0.3×10⁻⁵ or lower. In some embodiments, the method comprisesbase calls, where each base call represents a single nucleotide in thenucleic acid template; the ratio of the number of nucleic acid templatesto the number of polynucleotide B species is greater than 1,000,000,900,000, 800,000, 700,000, 600,000, 500,000, 400,000, 300,000, 200,000,100,000, 80,000, 60,000, 40,000, 20,000, 10,000 to 1; and the frequencyof base call errors is 1×10⁻⁴ or lower. In some embodiments, the methodcomprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates to the number of polynucleotide B species isgreater than 1,000,000, 900,000, 800,000, 700,000, 600,000, 500,000,400,000, 300,000, 200,000, 100,000, 80,000, 60,000, 40,000, 20,000,10,000 to 1; and the frequency of base call errors is 0.8×10⁻⁴ or lower.In some embodiments, the method comprises base calls, where each basecall represents a single nucleotide in the nucleic acid template; theratio of the number of nucleic acid templates to the number ofpolynucleotide B species is greater than 1,000,000, 900,000, 800,000,700,000, 600,000, 500,000, 400,000, 300,000, 200,000, 100,000, 80,000,60,000, 40,000, 20,000, 10,000 to 1; and the frequency of base callerrors is 0.5×10⁻⁴ or lower. In some embodiments, the method comprisesbase calls, where each base call represents a single nucleotide in thenucleic acid template; the ratio of the number of nucleic acid templatesto the number of polynucleotide B species is greater than 1,000,000,900,000, 800,000, 700,000, 600,000, 500,000, 400,000, 300,000, 200,000,100,000, 80,000, 60,000, 40,000, 20,000, 10,000 to 1; and the frequencyof base call errors is 0.3×10⁻⁴ or lower. In some embodiments, themethod comprises base calls, where each base call represents a singlenucleotide in the nucleic acid template; the ratio of the number ofnucleic acid templates to the number of polynucleotide B species isgreater than 1,000,000, 900,000, 800,000, 700,000, 600,000, 500,000,400,000, 300,000, 200,000, 100,000, 80,000, 60,000, 40,000, 20,000,10,000 to 1; and the frequency of base call errors is lower than 1×10⁻³.In some embodiments, the method comprises base calls, where each basecall represents a single nucleotide in the nucleic acid template; theratio of the number of nucleic acid templates to the number ofpolynucleotide B species is greater than 1,000,000, 900,000, 800,000,700,000, 600,000, 500,000, 400,000, 300,000, 200,000, 100,000, 80,000,60,000, 40,000, 20,000, 10,000 to 1; and the frequency of base callerrors is 1×10⁻³ or lower.

In some embodiments, the presence of a single nucleotide alteration inthe nucleic acid is determined; there are 999 or fewer polynucleotide Bspecies; and the single nucleotide alteration is present at a frequencyof 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5, or 1 percent or lower. In someembodiments, the single nucleotide alteration is present at a frequencylower than 1 percent. In some embodiments, the single nucleotidealteration is present at a frequency of 1, 0.75, 0.5, 0.25, 0.1, 0.075,0.05, 0.025, 0.01, 0.0075, 0.005, 0.0025, 0.001, 0.00075, 0.0005,0.00025, or 0.0001 percent or lower. In some embodiments, the presenceof a single nucleotide alteration in the nucleic acid is determined;there are 500 or fewer polynucleotide B species; and the singlenucleotide alteration is present at a frequency of 5, 4.5, 4, 3.5, 3,2.5, 2, 1.5, or 1 percent or lower. In some embodiments, the singlenucleotide alteration is present at a frequency lower than 1 percent. Insome embodiments, the single nucleotide alteration is present at afrequency of 1, 0.75, 0.5, 0.25, 0.1, 0.075, 0.05, 0.025, 0.01, 0.0075,0.005, 0.0025, 0.001, 0.00075, 0.0005, 0.00025, or 0.0001 percent orlower. In some embodiments, the presence of a single nucleotidealteration in the nucleic acid is determined; there are 400 or fewerpolynucleotide B species; and the single nucleotide alteration ispresent at a frequency of 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5, or 1 percentor lower. In some embodiments, the single nucleotide alteration ispresent at a frequency lower than 1 percent. In some embodiments, thesingle nucleotide alteration is present at a frequency of 1, 0.75, 0.5,0.25, 0.1, 0.075, 0.05, 0.025, 0.01, 0.0075, 0.005, 0.0025, 0.001,0.00075, 0.0005, 0.00025, or 0.0001 percent or lower. In someembodiments, the presence of a single nucleotide alteration in thenucleic acid is determined; there are 300 or fewer polynucleotide Bspecies; and the single nucleotide alteration is present at a frequencyof 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5, or 1 percent or lower. In someembodiments, the single nucleotide alteration is present at a frequencylower than 1 percent. In some embodiments, the single nucleotidealteration is present at a frequency of 1, 0.75, 0.5, 0.25, 0.1, 0.075,0.05, 0.025, 0.01, 0.0075, 0.005, 0.0025, 0.001, 0.00075, 0.0005,0.00025, or 0.0001 percent or lower. In some embodiments, the presenceof a single nucleotide alteration in the nucleic acid is determined;there are about 200 to about 300 polynucleotide B species; and thesingle nucleotide alteration is present at a frequency of 5, 4.5, 4,3.5, 3, 2.5, 2, 1.5, or 1 percent or lower. In some embodiments, thesingle nucleotide alteration is present at a frequency lower than 1percent. In some embodiments, the single nucleotide alteration ispresent at a frequency of 1, 0.75, 0.5, 0.25, 0.1, 0.075, 0.05, 0.025,0.01, 0.0075, 0.005, 0.0025, 0.001, 0.00075, 0.0005, 0.00025, or 0.0001percent or lower.

In some embodiments, the presence of a single nucleotide alteration inthe nucleic acid is determined; there are about 280 to about 290polynucleotide B species; and the single nucleotide alteration ispresent at a frequency of 5, 4.5, 4, 3.5, 3, 2.5, 2, 1.5, or 1 percentor lower. In some embodiments, the single nucleotide alteration ispresent at a frequency lower than 1 percent. In some embodiments, thesingle nucleotide alteration is present at a frequency of 1, 0.75, 0.5,0.25, 0.1, 0.075, 0.05, 0.025, 0.01, 0.0075, 0.005, 0.0025, 0.001,0.00075, 0.0005, 0.00025, or 0.0001 percent or lower.

Error Detection

In some embodiments of the methods and systems provided herein, thesequence of at least one polynucleotide B species and the sequence of atleast one polynucleotide B′ species are determined and are utilized toidentify one or more errors. In some embodiments, the errors aresequencing errors and/or amplification errors. In some embodiments, thesequencing generates sequence reads and the sequence reads are mapped toregions of a reference genome. Thus, as discussed above, the use of thedisclosed Y adapters can provide for increased specificity andsensitivity in DNA sequencing reactions. The use of double-stranded Yadapters allows for the two strands (i.e., forward and reverse) of eachDNA template to be identified as being from the same molecule. In thisway, if there is a detected base change at a particular position in theDNA template on one strand that is not replicated for the other strand,it can be inferred that the base change is due to artifactual errorsintroduced during sample processing including, but not limited to,sequencing errors, and is not a true mutation.

In certain embodiments, the methods comprise sequencing amplifiedadapter-ligated nucleic acid templates for a nucleic acid sample. Incertain embodiments, the methods and/or systems comprise reviewingsequences obtained for amplified adapter-ligated nucleic acid templatesfor a nucleic acid sample. Such review may comprise identifying a set ofamplicon duplicates, where the amplicon duplicates comprise amplifiedadapter-ligated nucleic acid templates comprising at least one nonrandomoligonucleotide adapter comprising a polynucleotide B species; anddetermining the sequence of nucleotides for the template by removingfrom the determination of the sequence nucleic acid sequences having oneor more nucleotide positions that disagree with the nucleotide positiondetermined in about 95% or more of the nucleic acid sequences of the setof amplicon duplicates. In some embodiments, amplicons having the samelength are selected for the set of amplicon duplicates.

In some embodiments, the methods and/or systems comprise the steps ofand/or software to implement the steps of reviewing the sequence ofnucleotides for one or more nucleic acid templates in a nucleic acidsample, comprising (a) identifying a first set of amplicon duplicates,where the amplicon duplicates comprise amplified adapter-ligated nucleicacid templates comprising a first polynucleotide B species at a firstend and a second polynucleotide B species at the second end; (b)identifying a second set of amplicon duplicates, where the second set ofamplicon duplicates comprise amplified adapter-ligated nucleic acidtemplates comprising the B′ species that are the reverse complement ofthe first and second B species of step (a); (c) obtaining a first singlestrand consensus sequence for the first set of amplicon duplicates, anda second single strand consensus sequence for the second set of ampliconduplicates; and (d) determining the sequence of nucleotides for the oneor more nucleic acid templates in a nucleic acid sample by removing fromthe determination of the sequence nucleic acid sequences having one ormore nucleotide positions where the first single strand consensussequence and the second single strand consensus sequence disagree at oneor more nucleotide positions. By “disagree” is meant that the nucleotideidentified at a position in the first single strand consensus sequencediffers from nucleotide identified at the corresponding position in thesecond single strand consensus sequence. Where the two single strandconsensus sequences are complementary, it is understood that by “differ”or “disagree” is meant that the nucleotide identified at a position inthe first single strand consensus sequence, is not complementary to thenucleotide identified at the corresponding position in the second singlestrand consensus sequence.

In some embodiments of the methods, compositions and/or systems providedherein, each of the adapter-ligated nucleic acid templates comprises afirst nonrandom oligonucleotide adapter at a first end and a secondnonrandom oligonucleotide adapter at a second end, and the methodsand/or systems comprise reviewing the sequence of nucleotides for one ormore nucleic acid templates in a nucleic acid sample, comprising (a)identifying a first set of amplicon duplicates, where the ampliconduplicates comprise amplified adapter-ligated nucleic acid templatescomprising a first polynucleotide B species and a second polynucleotideB species; (b) identifying a second set of amplicon duplicates, wherethe second set of amplicon duplicates comprise amplified adapter-ligatednucleic acid templates comprising the B′ species that are the reversecomplement of the first and second B species of step (a); (c) sequencingall or a portion of the sequences of each amplicon of the first set andsecond set of amplicon duplicates; (d) determining the sequence ofnucleotides for the one or more nucleic acid templates in a nucleic acidsample by removing from the determination of the sequence nucleic acidsequences having one or more nucleotide positions where the nucleic acidstrand for the first set of amplicon duplicates and the nucleic acidstrand for the second set of amplicon duplicates disagree at one or morenucleotide positions. In some embodiments, amplicons having the samelength are selected for the first and second set of amplicon duplicates.

In some embodiments, the method and or system further comprises thesteps (and/or software to implement such steps) of obtaining a list of Bspecies and B′ species of the nonrandom oligonucleotide adaptersprovided for ligation with the nucleic acid templates; determining thesequence of the B species or B′ species of the nonrandom oligonucleotideadapter-ligated nucleic acid templates; comparing the sequence of the Bspecies or B′ species to the sequences of the B and B′ species on thelist; and removing from the determination of the sequence of the nucleicacid templates, adapter-ligated nucleic acid templates that comprise Bspecies or B′ species sequences that are not identical to a B species orB′ species sequence on the list. By “list” is meant any record of the Bor B′ species or B or B′ species sequences provided in the ligationreaction with the nucleic acid templates, either in the form of adatabase, or electronic or physical document, and also includes anyrecord of which B or B′ species or B or B′ species sequences wereincluded in the ligation reaction, such as, for example, a physicalsample of the B species. In some embodiments, some of the sequences ofadapter-ligated nucleic acid templates that comprise B species or B′species sequences that are not identical to a B species or B′ species onthe list are not removed from the sequence determination, and areinstead assigned a weight, where the assigned weight is considered inthe determination of at least one base call. For example, in someembodiments, nonrandom oligonucleotide adapter-ligated nucleic acidsequences comprising a B species sequence or a B′ species sequence thatis identical to a B species sequence or a B′ species sequence providedin the list of step (a) are assigned a weight of 1; nonrandomoligonucleotide adapter-ligated nucleic acid sequences comprising a Bspecies sequence or a B′ species sequence that comprises or consists ofone nucleotide difference from a B species sequence or B′ speciessequence provided in the list of step (a) are assigned a weight of lessthan 1, and greater than 0, for example, 0.5, and nonrandomoligonucleotide adapter-ligated nucleic acid sequences comprising a Bspecies sequence or a B′ species sequence that comprise more than onenucleotide difference from a B species sequence or a B′ species sequenceprovided in the list of step (a) are assigned a weight of 0. It isunderstood that the weights provided herein are provided as examples,and may include, for example, relative weights.

Quantifying DNA Templates

In some embodiments of the methods and/or systems provided herein, themethods comprise steps and/or software to implement such steps toquantifying the nucleic acid templates for the nucleic acid sample.Quantification methods include, for example, quantifying molecules thatcomprise at least one single nucleotide alteration, and also includemethods for quantifying nucleic acid templates, for example, to detectcopy number alterations through relative abundance. For someembodiments, accurately counting the number of template molecules canimprove the accuracy of the measurement, for example in counting-basedmethods for the detection of copy number alterations. Such embodimentsmay be used for detection or monitoring alterations for use innoninvasive prenatal testing (NIPT), cancer, or any disorder where copynumber alterations are relevant for the disease. These embodiments may,for example, comprise (a) identifying a set of amplicon duplicates,where the amplicon duplicates comprise amplified adapter-ligated nucleicacid templates comprising a polynucleotide B species; and (b)determining the number of amplicon duplicates comprising thepolynucleotide B species. In some embodiments, the methods and/orsystems comprise determining a base call of at least one nucleotide of anucleic acid template, comprising (a) identifying a set of ampliconduplicates, where the amplicon duplicates comprise amplifiedadapter-ligated nucleic acid templates comprising a polynucleotide Bspecies; (b) identifying the at least one nucleotide in each amplicon ofthe set of amplicon duplicates; (c) determining the base call of the atleast one nucleotide where the identity of the at least one nucleotideis the same in at least 95% of the amplicons in the set of ampliconduplicates. In some embodiments, the methods and/or systems comprisequantifying the nucleic acid templates for the nucleic acid sample thatcomprise the base call of the at least one nucleotide. In someembodiments, amplicons having the same length are selected for the setof amplicon duplicates.

In some embodiments, methods and/or systems are provided for countingthe nucleic acid templates for the nucleic acid sample, comprisingidentifying a set of amplicon duplicates, where the amplicon duplicatescomprise amplified adapter-ligated nucleic acid templates comprising apolynucleotide B species on one strand and determining the number ofamplicon duplicates comprising the polynucleotide B species. In someembodiments, the methods and/or systems comprise comparing the number offirst amplicon duplicates comprising a first polynucleotide B specieswith the number of second amplicon duplicates comprising a secondpolynucleotide B species. In some embodiments, the first ampliconduplicates comprise copies of a first nucleic acid template of a firstchromosome and the second amplicon duplicates comprise copies of asecond nucleic acid template of a second chromosome.

Also provided are methods and/or systems for implementing such methodscomprising counting the number of nucleic acid templates for the nucleicacid sample, comprising identifying the nonrandom oligonucleotideadapter species ligated to each nucleic acid template; and counting thenumber of nonrandom oligonucleotide adapter species ligated to thenucleic acid templates for the nucleic acid sample. By unique nucleicacid template is meant a nucleic acid templates, that is, for example, anucleic acid template present in the sample. The nucleic acid templatemay have, in some embodiments, a particular nucleotide sequence that isnot the same as another nucleic acid template nucleotide sequence, or isobtained from a different chromosomal location. For example, two nucleicacid template species include nucleotide sequences that differ by atleast one nucleotide, that is, one nucleic acid template species differsfrom another nucleic acid template species where the nucleic acidtemplates differ by at least one nucleotide.

In some embodiments, methods and/or systems are provided for countingthe nucleic acid templates for the nucleic acid sample, comprisingidentifying a set of amplicon duplicates, where the amplicon duplicatescomprise amplified adapter-ligated nucleic acid templates comprising afirst polynucleotide B species and a second polynucleotide B species,where the first and second polynucleotide B species may or may notconsist of the same nucleotide sequence; and determining the number ofamplicon duplicates comprising both the first and the secondpolynucleotide B species. In some embodiments, the methods and/orsystems comprise comparing the number of first amplicon duplicatescomprising the first and second polynucleotide B species on one strandwith the number of second amplicon duplicates, where the second ampliconduplicates comprise amplified adapter-ligated nucleic acid templatescomprising a third polynucleotide B species and a fourth polynucleotidespecies on one strand, where the third and fourth polynucleotide Bspecies may or may not consist of the same nucleotide sequence. In someembodiments, the first amplicon duplicates comprise copies of a firstnucleic acid template of a first chromosome and the second ampliconduplicates comprise copies of a second nucleic acid template of a secondchromosome.

Also provided are methods and/or systems for implementing such methodscomprising counting the number of unique nucleic acid templates for thenucleic acid sample, comprising identifying the nonrandomoligonucleotide adapter species pairs, where the nonrandomoligonucleotide adapter species pairs consist of a first nonrandomoligonucleotide adapter ligated to the nucleic acid template and asecond nonrandom oligonucleotide adapter ligated to the nucleic acidtemplate; and counting the number of nonrandom oligonucleotide adapterspecies pairs.

In some embodiments, each of the nonrandom oligonucleotideadapter-ligated nucleic acid templates comprises a first nonrandomoligonucleotide adapter and a second nonrandom oligonucleotide adapterand the methods and/or systems comprise determining a base call of atleast one nucleotide of a nucleic acid template, comprising (a)identifying a first set of amplicon duplicates, where the ampliconduplicates comprise amplified adapter-ligated nucleic acid templatescomprising a first polynucleotide B species and a second polynucleotideB species on one strand; (b) identifying a second set of ampliconduplicates, where the second set of amplicon duplicates compriseamplified nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprising the B′ species that are the reverse complement ofthe first and second B species of step (a); (c) identifying the at leastone nucleotide in each amplicon of the first set, and identifying the atleast one nucleotide at the complementary position in the second set ofamplicon duplicates; (d) determining the base call of the at least onenucleotide where the identity of the at least one nucleotide is the samein at least 95% of the amplicons in the first set or the second set ofamplicon duplicates; and the identity of the at least one nucleotide inthe first set of amplicon duplicates is the complement to the identityof the at least one nucleotide in the complementary position in thesecond set of amplicon duplicates. In some embodiments, amplicons havingthe same length are selected for the first and second set of ampliconduplicates.

As used herein, it is understood that the use of polynucleotide B andpolynucleotide B′ may be used interchangeably, in that one strand of adouble-stranded adapter-ligated nucleic acid template may comprise, forexample, a first nonrandom oligonucleotide adapter comprising a firstpolynucleotide B species and the complementary first polynucleotide B′species, and a second nonrandom oligonucleotide adapter comprising asecond polynucleotide B species and the complementary secondpolynucleotide B′ species. A first strand of the adapter-ligated nucleicacid template may therefore comprise the first polynucleotide B speciesand the second polynucleotide B′ species, while the second strand of theadapter-ligated nucleic acid template may comprise the firstpolynucleotide B′ species and the second polynucleotide B species. Thus,in the present methods for counting or sequencing the nucleic acidtemplates, where the number of first amplicon duplicates comprising thefirst and second polynucleotide B species on one strand are counted, itis understood that in the context of discussion of one strand, one ofthe polynucleotide B species may consist of what might otherwise beconsidered to be a polynucleotide B′ species when viewed in the contextof the double stranded adapter-ligated nucleic acid template.

In some embodiments of the methods provided herein, each of thenonrandom oligonucleotide adapter-ligated nucleic acid templatescomprises a first nonrandom oligonucleotide adapter and a secondnonrandom oligonucleotide adapter and the methods comprise determining abase call of at least one nucleotide of a nucleic acid template,comprising (a) identifying a first set of amplicon duplicates, where theamplicon duplicates comprise amplified adapter-ligated nucleic acidtemplates comprising a first polynucleotide B species and a secondpolynucleotide B species (for example, a first polynucleotide B speciesand a first polynucleotide B′ species on one strand); (b) identifying asecond set of amplicon duplicates, where the second set of ampliconduplicates comprise amplified nonrandom oligonucleotide adapter-ligatednucleic acid templates comprising the B′ species that are the reversecomplement of the first and second B species of step (a) (for example,the reverse complements of the first polynucleotide B species and thefirst polynucleotide B′ species); (c) identifying the at least onenucleotide in each amplicon of the first set, and identifying the atleast one nucleotide at the complementary position in the second set ofamplicon duplicates; (d) determining the base call of the at least onenucleotide where the identity of the at least one nucleotide is the samein at least 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%of the amplicons in the first set or the second set of ampliconduplicates; and the identity of the at least one nucleotide in the firstset of amplicon duplicates is the complement to the identity of the atleast one nucleotide in the complementary position in the second set ofamplicon duplicates. In some embodiments, amplicons having the samelength are selected for the first and second set of amplicon duplicates.

In some embodiments, the counting method further comprises the steps ofobtaining a list of B species and B′ species of the nonrandomoligonucleotide adapters provided for ligation with the nucleic acidtemplates; determining the sequence of the B species or B′ species ofthe nonrandom oligonucleotide adapter-ligated nucleic acid templates;comparing the sequence of the B species or B′ species to the sequencesof the B and B′ species on the list; and removing from the determinationof the count of nucleic acid templates, adapter-ligated nucleic acidtemplates that comprise B species or B′ species sequences that are notidentical to a B species or B′ species sequence on the list. By “list”is meant any record of the B or B′ species or B or B′ species sequencesprovided in the ligation reaction with the nucleic acid templates,either in the form of a database, or electronic or physical document,and also includes any record of which B or B′ species or B or B′ speciessequences were included in the ligation reaction, such as, for example,a physical sample of the B species. In some embodiments, some of theadapter-ligated nucleic acid templates that comprise B species or B′species sequences that are not identical to a B species or B′ species onthe list are not removed from the sequence determination, and areinstead assigned a weight, where the assigned weight is considered inthe counting of at least one nucleic acid template. For example, in someembodiments, nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprising a B species sequence or a B′ species sequence thatis identical to a B species sequence or a B′ species sequence providedin the list of step (a) are assigned a weight of 1; nonrandomoligonucleotide adapter-ligated nucleic acid templates comprising a Bspecies sequence or a B′ species sequence that comprises or consists ofone nucleotide difference from a B species sequence or B′ speciessequence provided in the list of step (a) are assigned a weight of lessthan 1, and greater than 0, for example, 0.5, and nonrandomoligonucleotide adapter-ligated nucleic acid templates comprising a Bspecies sequence or a B′ species sequence that comprise more than onenucleotide difference from a B species sequence or a B′ species sequenceprovided in the list of step (a) are assigned a weight of 0. It isunderstood that the weights provided herein are provided as examples,and may include, for example, relative weights.

Manufacturing Sets of Nonrandom Nucleic Acid Sequencing Adapters

In certain embodiments, provided herein are methods for manufacturing aset of 999 or fewer, or 900 or fewer, or 800 or fewer, or 700 or fewer,or 600 or fewer, or 500 or fewer, or 400 or fewer, or 300 or fewer(e.g., 288), or 200 or fewer, or 100 or fewer nonrandom nucleic acidsequencing adapters (nonrandom oligonucleotide adapters), for use indetermining a sequence of nucleotides for one or more nucleic acidtemplates in a nucleic acid sample. The set of adapters may bemanufactured for use in sequencing where the ratio of the nonrandomnucleic acid sequencing adapter molecules to nucleic acid templates ofthe nucleic acid sample is greater than 10 to 1, 15 to 1, 20 to 1, 25 to1, 30 to 1, 35 to 1, 40 to 1, 45 to 1, or 50 to 1. The method maycomprise or consisting essentially of: providing first oligonucleotidespecies and second oligonucleotide species; where: each of the firstoligonucleotide species comprises 5′ to 3′ a polynucleotide A and apolynucleotide B species and each of the second oligonucleotide speciescomprises 5′ to 3′ a polynucleotide B′ species and a polynucleotide A′;each of the polynucleotide B species and the polynucleotide B′ speciesare predetermined, are non-randomly generated, are the same length, andare about 4 to about 20 consecutive nucleotides in length; there are 999or fewer polynucleotide B species and each polynucleotide B′ species isa reverse complement of a polynucleotide B species; polynucleotide A isnot a reverse complement of polynucleotide A′. In certain embodimentseach of the first oligonucleotide species and each of the secondoligonucleotide species is been synthesized separately; and in separatepairs. The method may further include contacting each firstoligonucleotide species with each second oligonucleotide speciescomprising the reverse complement polynucleotide B′ species underannealing conditions, thereby generating partially double-strandedadapter species; where the polynucleotide B species are annealed tocomplementary polynucleotide B′ species and polynucleotide A′ is notannealed to polynucleotide A.

In some embodiments, the ratio of the nonrandom oligonucleotide adaptermolecules to nucleic acid templates of the nucleic acid sample isdesigned to bes greater than 10 to 1. In some embodiments, the ratio ofthe nucleic acid sequencing adapter molecules to nucleic acid templatesof the nucleic acid sample is greater than 20 to 1. In some embodiments,the ratio of the nucleic acid sequencing adapter molecules to nucleicacid templates of the nucleic acid sample is greater than 30 to 1.

In some embodiments, the set of nonrandom oligonucleotide adapters arecombined in a vessel.

In some embodiments, the polynucleotide B species and the polynucleotideB′ species are non-degenerate polynucleotides. In some embodiments, thepolynucleotide B species and the polynucleotide B′ species arenon-degenerate or non-semidegenerate polynucleotides.

In some embodiments, the polynucleotide B species and the polynucleotideB′ species are about 6 to about 10 consecutive nucleotide bases inlength. In some embodiments, the polynucleotide B species and thepolynucleotide B′ species are about 8 consecutive nucleotide bases inlength.

In some embodiments, there are 999 or fewer polynucleotide B species. Insome embodiments, there are 400 or fewer polynucleotide B species. Insome embodiments, there are 300 or fewer polynucleotide B species. Insome embodiments, there are about 300 to about 400 polynucleotide Bspecies. In some embodiments, there are about 100 to about 500polynucleotide B species. In some embodiments, there are about 200 to300 polynucleotide B species. In some embodiments, there are about 280to about 290 polynucleotide B species. In some embodiments, there are1000, 750, 500, 475, 450, 425, 400, 375, 350, 325, 300, 275, 250, 225,200, 175, 150, 125, or 100 or fewer polynucleotide B species.

Samples

Provided herein are systems, methods and products for analyzing nucleicacids. In some embodiments, nucleic acid templates in a mixture ofnucleic acid templates are analyzed. A mixture of nucleic acids cancomprise two or more nucleic acid templates having the same or differentnucleotide sequences, different template lengths, different origins(e.g., genomic origins, fetal vs. maternal origins, cell or tissueorigins, cancer vs. non-cancer origin, tumor vs. non-tumor origin,sample origins, subject origins, and the like), or combinations thereof.

Nucleic acid or a nucleic acid mixture utilized in systems, methods andproducts described herein often is isolated from a sample obtained froma subject (e.g., a test subject). A subject can be any living ornon-living organism, including but not limited to a human, a non-humananimal, a plant, a bacterium, a fungus, a protest or a pathogen. Anyhuman or non-human animal can be selected, and may include, for example,mammal, reptile, avian, amphibian, fish, ungulate, ruminant, bovine(e.g., cattle), equine (e.g., horse), caprine and ovine (e.g., sheep,goat), swine (e.g., pig), camelid (e.g., camel, llama, alpaca), monkey,ape (e.g., gorilla, chimpanzee), ursid (e.g., bear), poultry, dog, cat,mouse, rat, fish, dolphin, whale and shark. A subject may be a male orfemale (e.g., woman, a pregnant woman). A subject may be any age (e.g.,an embryo, a fetus, an infant, a child, an adult). A subject may be acancer patient, a patient suspected of having cancer, a patient inremission, a patient with a family history of cancer, and/or a subjectobtaining a cancer screen. In some embodiments, a test subject is afemale. In some embodiments, a test subject is a human female. In someembodiments, a test subject is a male. In some embodiments, a testsubject is a human male.

Nucleic acid may be isolated from any type of suitable biologicalspecimen or sample (e.g., a test sample). A sample or test sample can beany specimen that is isolated or obtained from a subject or part thereof(e.g., a human subject, a pregnant female, a cancer patient, a fetus, atumor). Non-limiting examples of specimens include fluid or tissue froma subject, including, without limitation, blood or a blood product(e.g., serum, plasma, or the like), umbilical cord blood, chorionicvilli, amniotic fluid, cerebrospinal fluid, spinal fluid, lavage fluid(e.g., bronchoalveolar, gastric, peritoneal, ductal, ear, arthroscopic),biopsy sample (e.g., from pre-implantation embryo; cancer biopsy),celocentesis sample, cells (blood cells, placental cells, embryo orfetal cells, fetal nucleated cells or fetal cellular remnants, normalcells, abnormal cells (e.g., cancer cells)) or parts thereof (e.g.,mitochondrial, nucleus, extracts, or the like), washings of femalereproductive tract, urine, feces, sputum, saliva, nasal mucous, prostatefluid, lavage, semen, lymphatic fluid, bile, tears, sweat, breast milk,breast fluid, the like or combinations thereof. In some embodiments, abiological sample is a cervical swab from a subject. A fluid or tissuesample from which nucleic acid is extracted may be acellular (e.g.,cell-free). In some embodiments, a fluid or tissue sample may containcellular elements or cellular remnants. In some embodiments, fetal cellsor cancer cells may be included in the sample.

A sample can be a liquid sample. A liquid sample can compriseextracellular nucleic acid (e.g., circulating cell-free DNA).Non-limiting examples of liquid samples, include, blood or a bloodproduct (e.g., serum, plasma, or the like), urine, biopsy sample (e.g.,liquid biopsy for the detection of cancer), a liquid sample describedabove, the like or combinations thereof. In certain embodiments, asample is a liquid biopsy, which generally refers to an assessment of aliquid sample from a subject for the presence, absence, progression orremission of a disease (e.g., cancer). A liquid biopsy can be used inconjunction with, or as an alternative to, a sold biopsy (e.g., tumorbiopsy). In certain instances, extracellular nucleic acid is analyzed ina liquid biopsy.

In some embodiments, a biological sample may be blood, plasma or serum.The term “blood” encompasses whole blood, blood product or any fractionof blood, such as serum, plasma, buffy coat, or the like asconventionally defined. Blood or fractions thereof often comprisenucleosomes. Nucleosomes comprise nucleic acids and are sometimescell-free or intracellular. Blood also comprises buffy coats. Buffycoats are sometimes isolated by utilizing a ficoll gradient. Buffy coatscan comprise white blood cells (e.g., leukocytes, T-cells, B-cells,platelets, and the like). Blood plasma refers to the fraction of wholeblood resulting from centrifugation of blood treated withanticoagulants. Blood serum refers to the watery portion of fluidremaining after a blood sample has coagulated. Fluid or tissue samplesoften are collected in accordance with standard protocols hospitals orclinics generally follow. For blood, an appropriate amount of peripheralblood (e.g., between 3 to 40 milliliters, between 5 to 50 milliliters)often is collected and can be stored according to standard proceduresprior to or after preparation.

An analysis of nucleic acid found in a subject's blood may be performedusing, e.g., whole blood, serum, or plasma. An analysis of fetal DNAfound in maternal blood, for example, may be performed using, e.g.,whole blood, serum, or plasma. An analysis of tumor DNA found in apatient's blood, for example, may be performed using, e.g., whole blood,serum, or plasma. Methods for preparing serum or plasma from bloodobtained from a subject (e.g., a maternal subject; cancer patient) areknown. For example, a subject's blood (e.g., a pregnant woman's blood;cancer patient's blood) can be placed in a tube containing EDTA or aspecialized commercial product such as Vacutainer SST (Becton Dickinson,Franklin Lakes, N.J.) to prevent blood clotting, and plasma can then beobtained from whole blood through centrifugation. Serum may be obtainedwith or without centrifugation-following blood clotting. Ifcentrifugation is used then it is typically, though not exclusively,conducted at an appropriate speed, e.g., 1,500-3,000 times g. Plasma orserum may be subjected to additional centrifugation steps before beingtransferred to a fresh tube for nucleic acid extraction. In addition tothe acellular portion of the whole blood, nucleic acid may also berecovered from the cellular fraction, enriched in the buffy coatportion, which can be obtained following centrifugation of a whole bloodsample from the subject and removal of the plasma.

A sample may be heterogeneous. For example, a sample may include morethan one cell type and/or one or more nucleic acid species. In someinstances, a sample may include (i) fetal cells and maternal cells, (ii)cancer cells and non-cancer cells, and/or (iii) pathogenic cells andhost cells. In some instances, a sample may include (i) cancer andnon-cancer nucleic acid, (ii) pathogen and host nucleic acid, (iii)fetal derived and maternal derived nucleic acid, and/or more generally,(iv) mutated and wild-type nucleic acid. In some instances, a sample mayinclude a minority nucleic acid species and a majority nucleic acidspecies, as described in further detail below. In some instances, asample may include cells and/or nucleic acid from a single subject ormay include cells and/or nucleic acid from multiple subjects.

Cell Types

As used herein, a “cell type” refers to a type of cell that can bedistinguished from another type of cell. Extracellular nucleic acid caninclude nucleic acid from several different cell types. Non-limitingexamples of cell types that can contribute nucleic acid to circulatingcell-free nucleic acid include liver cells (e.g., hepatocytes), lungcells, spleen cells, pancreas cells, colon cells, skin cells, bladdercells, eye cells, brain cells, esophagus cells, cells of the head, cellsof the neck, cells of the ovary, cells of the testes, prostate cells,placenta cells, epithelial cells, endothelial cells, adipocyte cells,kidney/renal cells, heart cells, muscle cells, blood cells (e.g., whiteblood cells), central nervous system (CNS) cells, the like andcombinations of the foregoing. In some embodiments, cell types thatcontribute nucleic acid to circulating cell-free nucleic acid analyzedinclude white blood cells, endothelial cells and hepatocyte liver cells.Different cell types can be screened as part of identifying andselecting nucleic acid loci for which a marker state is the same orsubstantially the same for a cell type in subjects having a medicalcondition and for the cell type in subjects not having the medicalcondition, as described in further detail herein.

A particular cell type sometimes remains the same or substantially thesame in subjects having a medical condition and in subjects not having amedical condition. In a non-limiting example, the number of living orviable cells of a particular cell type may be reduced in a celldegenerative condition, and the living, viable cells are not modified,or are not modified significantly, in subjects having the medicalcondition.

A particular cell type sometimes is modified as part of a medicalcondition and has one or more different properties than in its originalstate. In a non-limiting example, a particular cell type may proliferateat a higher than normal rate, may transform into a cell having adifferent morphology, may transform into a cell that expresses one ormore different cell surface markers and/or may become part of a tumor,as part of a cancer condition. In embodiments for which a particularcell type (i.e., a progenitor cell) is modified as part of a medicalcondition, the marker state for each of the one or more markers assayedoften is the same or substantially the same for the particular cell typein subjects having the medical condition and for the particular celltype in subjects not having the medical condition. Thus, the term “celltype” sometimes pertains to a type of cell in subjects not having amedical condition, and to a modified version of the cell in subjectshaving the medical condition. In some embodiments, a “cell type” is aprogenitor cell only and not a modified version arising from theprogenitor cell. A “cell type” sometimes pertains to a progenitor celland a modified cell arising from the progenitor cell. In suchembodiments, a marker state for a marker analyzed often is the same orsubstantially the same for a cell type in subjects having a medicalcondition and for the cell type in subjects not having the medicalcondition.

In certain embodiments, a cell type is a cancer cell. Certain cancercell types include, for example, leukemia cells (e.g., acute myeloidleukemia, acute lymphoblastic leukemia, chronic myeloid leukemia,chronic lymphoblastic leukemia); cancerous kidney/renal cells (e.g.,renal cell cancer (clear cell, papillary type 1, papillary type 2,chromophobe, oncocytic, collecting duct), renal adenocarcinoma,hypernephroma, Wilm's tumor, transitional cell carcinoma); brain tumorcells (e.g., acoustic neuroma, astrocytoma (grade I: pilocyticastrocytoma, grade II: low-grade astrocytoma, grade III: anaplasticastrocytoma, grade IV: glioblastoma (GBM)), chordoma, cns lymphoma,craniopharyngioma, glioma (brain stem glioma, ependymoma, mixed glioma,optic nerve glioma, subependymoma), medulloblastoma, meningioma,metastatic brain tumors, oligodendroglioma, pituitary tumors, primitiveneuroectodermal (PNET), schwannoma, juvenile pilocytic astrocytoma(JPA), pineal tumor, rhabdoid tumor).

Different cell types can be distinguished by any suitablecharacteristic, including without limitation, one or more different cellsurface markers, one or more different morphological features, one ormore different functions, one or more different protein (e.g., histone)modifications and one or more different nucleic acid markers.Non-limiting examples of nucleic acid markers include single-nucleotidepolymorphisms (SNPs), methylation state of a nucleic acid locus, shorttandem repeats, insertions (e.g., microinsertions), deletions(microdeletions) the like and combinations thereof Non-limiting examplesof protein (e.g., histone) modifications include acetylation,methylation, ubiquitylation, phosphorylation, sumoylation, the like andcombinations thereof.

As used herein, the term a “related cell type” refers to a cell typehaving multiple characteristics in common with another cell type. Inrelated cell types, 75% or more cell surface markers sometimes arecommon to the cell types (e.g., about 80%, 85%, 90% or 95% or more ofcell surface markers are common to the related cell types).

Nucleic Acid

Provided herein are methods for analyzing nucleic acid. The terms“nucleic acid,” “nucleic acid molecule,” “nucleic acid fragment,” and“nucleic acid template” may be used interchangeably throughout thedisclosure. The terms refer to nucleic acids of any composition from,such as DNA (e.g., complementary DNA (cDNA), genomic DNA (gDNA) and thelike), RNA (e.g., message RNA (mRNA), short inhibitory RNA (siRNA),ribosomal RNA (rRNA), tRNA, microRNA, RNA highly expressed by a fetus orplacenta, and the like), and/or DNA or RNA analogs (e.g., containingbase analogs, sugar analogs and/or a non-native backbone and the like),RNA/DNA hybrids and polyamide nucleic acids (PNAs), all of which can bein single- or double-stranded form, and unless otherwise limited, canencompass known analogs of natural nucleotides that can function in asimilar manner as naturally occurring nucleotides. A nucleic acid maybe, or may be from, a plasmid, phage, virus, bacterium, autonomouslyreplicating sequence (ARS), mitochondria, centromere, artificialchromosome, chromosome, or other nucleic acid able to replicate or bereplicated in vitro or in a host cell, a cell, a cell nucleus orcytoplasm of a cell in certain embodiments. A template nucleic acid insome embodiments can be from a single chromosome (e.g., a nucleic acidsample may be from one chromosome of a sample obtained from a diploidorganism). Unless specifically limited, the term encompasses nucleicacids containing known analogs of natural nucleotides that have similarbinding properties as the reference nucleic acid and are metabolized ina manner similar to naturally occurring nucleotides. Unless otherwiseindicated, a particular nucleic acid sequence also implicitlyencompasses conservatively modified variants thereof (e.g., degeneratecodon substitutions), alleles, orthologs, single nucleotidepolymorphisms (SNPs), and complementary sequences as well as thesequence explicitly indicated. Specifically, degenerate codonsubstitutions may be achieved by generating sequences in which the thirdposition of one or more selected (or all) codons is substituted withmixed-base and/or deoxyinosine residues. The term nucleic acid is usedinterchangeably with locus, gene, cDNA, and mRNA encoded by a gene. Theterm also may include, as equivalents, derivatives, variants and analogsof RNA or DNA synthesized from nucleotide analogs, single-stranded(“sense” or “antisense,” “plus” strand or “minus” strand, “forward”reading frame or “reverse” reading frame) and double-strandedpolynucleotides. The term “gene” refers to a section of DNA involved inproducing a polypeptide chain; and generally includes regions precedingand following the coding region (leader and trailer) involved in thetranscription/translation of the gene product and the regulation of thetranscription/translation, as well as intervening sequences (introns)between individual coding regions (exons). A nucleotide or basegenerally refers to the purine and pyrimidine molecular units of nucleicacid (e.g., adenine (A), thymine (T), guanine (G), and cytosine (C)).For RNA, the base thymine is replaced with uracil. Nucleic acid lengthor size may be expressed as a number of bases.

Nucleic acid may be single or double stranded. Single stranded DNA, forexample, can be generated by denaturing double stranded DNA by heatingor by treatment with alkali, for example. In certain embodiments,nucleic acid is in a D-loop structure, formed by strand invasion of aduplex DNA molecule by an oligonucleotide or a DNA-like molecule such aspeptide nucleic acid (PNA). D loop formation can be facilitated byaddition of E. coli RecA protein and/or by alteration of saltconcentration, for example, using methods known in the art.

Nucleic acid provided for processes described herein may contain nucleicacid from one sample or from two or more samples (e.g., from 1 or more,2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 ormore, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 ormore, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, or 20or more samples).

Nucleic acid may be derived from one or more sources (e.g., biologicalsample, blood, cells, serum, plasma, buffy coat, urine, lymphatic fluid,skin, soil, and the like) by methods known in the art. Any suitablemethod can be used for isolating, extracting and/or purifying DNA from abiological sample (e.g., from blood or a blood product), non-limitingexamples of which include methods of DNA preparation (e.g., described bySambrook and Russell, Molecular Cloning: A Laboratory Manual 3d ed.,2001), various commercially available reagents or kits, such as Qiagen'sQIAamp Circulating Nucleic Acid Kit, QiaAmp DNA Mini Kit or QiaAmp DNABlood Mini Kit (Qiagen, Hilden, Germany), GenomicPrep™ Blood DNAIsolation Kit (Promega, Madison, Wis.), and GFX™ Genomic Blood DNAPurification Kit (Amersham, Piscataway, N.J.), the like or combinationsthereof.

In some embodiments, nucleic acid is extracted from cells using a celllysis procedure. Cell lysis procedures and reagents are known in the artand may generally be performed by chemical (e.g., detergent, hypotonicsolutions, enzymatic procedures, and the like, or combination thereof),physical (e.g., French press, sonication, and the like), or electrolyticlysis methods. Any suitable lysis procedure can be utilized. Forexample, chemical methods generally employ lysing agents to disruptcells and extract the nucleic acids from the cells, followed bytreatment with chaotropic salts. Physical methods such as freeze/thawfollowed by grinding, the use of cell presses and the like also areuseful. In some instances, a high salt and/or an alkaline lysisprocedure may be utilized.

Nucleic acids can include extracellular nucleic acid in certainembodiments. The term “extracellular nucleic acid” as used herein canrefer to nucleic acid isolated from a source having substantially nocells and also is referred to as “cell-free” nucleic acid, “circulatingcell-free nucleic acid” (e.g., CCF fragments, ccf DNA) and/or “cell-freecirculating nucleic acid.” Extracellular nucleic acid can be present inand obtained from blood (e.g., from the blood of a human subject).Extracellular nucleic acid often includes no detectable cells and maycontain cellular elements or cellular remnants. Non-limiting examples ofacellular sources for extracellular nucleic acid are blood, bloodplasma, blood serum and urine. As used herein, the term “obtaincell-free circulating sample nucleic acid” includes obtaining a sampledirectly (e.g., collecting a sample, e.g., a test sample) or obtaining asample from another who has collected a sample. Without being limited bytheory, extracellular nucleic acid may be a product of cell apoptosisand cell breakdown, which provides basis for extracellular nucleic acidoften having a series of lengths across a spectrum (e.g., a “ladder”).In some embodiments, sample nucleic acid from a test subject iscirculating cell-free nucleic acid. In some embodiments, circulatingcell free nucleic acid is from blood plasma or blood serum from a testsubject.

Extracellular nucleic acid can include different nucleic acid species,and therefore is referred to herein as “heterogeneous” in certainembodiments. For example, blood serum or plasma from a person havingcancer can include nucleic acid from cancer cells (e.g., tumor,neoplasia) and nucleic acid from non-cancer cells. In another example,blood serum or plasma from a pregnant female can include maternalnucleic acid and fetal nucleic acid. In some instances, cancer or fetalnucleic acid sometimes is about 5% to about 50% of the overall nucleicacid (e.g., about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, or 49% of the totalnucleic acid is cancer or fetal nucleic acid).

At least two different nucleic acid species can exist in differentamounts in extracellular nucleic acid and sometimes are referred to asminority species and majority species. In certain instances, a minorityspecies of nucleic acid is from an affected cell type (e.g., cancercell, wasting cell, cell attacked by immune system). In certainembodiments, a genetic variation or genetic alteration (e.g., copynumber alteration, copy number variation, single nucleotide alteration,single nucleotide variation, chromosome alteration, and translocation)is determined for a minority nucleic acid species. In certainembodiments, a genetic variation or genetic alteration is determined fora majority nucleic acid species. Generally it is not intended that theterms “minority” or “majority” be rigidly defined in any respect. In oneaspect, a nucleic acid that is considered “minority,” for example, canhave an abundance of at least about 0.1% of the total nucleic acid in asample to less than 50% of the total nucleic acid in a sample. In someembodiments, a minority nucleic acid can have an abundance of at leastabout 1% of the total nucleic acid in a sample to about 40% of the totalnucleic acid in a sample. In some embodiments, a minority nucleic acidcan have an abundance of at least about 2% of the total nucleic acid ina sample to about 30% of the total nucleic acid in a sample. In someembodiments, a minority nucleic acid can have an abundance of at leastabout 3% of the total nucleic acid in a sample to about 25% of the totalnucleic acid in a sample. For example, a minority nucleic acid can havean abundance of about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%,13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%,27%, 28%, 29% or 30% of the total nucleic acid in a sample. In someinstances, a minority species of extracellular nucleic acid sometimes isabout 1% to about 40% of the overall nucleic acid (e.g., about 1%, 2%,3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%,19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%,33%, 34%, 35%, 36%, 37%, 38%, 39% or 40% of the nucleic acid is minorityspecies nucleic acid). In some embodiments, the minority nucleic acid isextracellular DNA. In some embodiments, the minority nucleic acid isextracellular DNA from apoptotic tissue. In some embodiments, theminority nucleic acid is extracellular DNA from tissue affected by acell proliferative disorder. In some embodiments, the minority nucleicacid is extracellular DNA from a tumor cell. In some embodiments, theminority nucleic acid is extracellular fetal DNA.

In another aspect, a nucleic acid that is considered “majority,” forexample, can have an abundance greater than 50% of the total nucleicacid in a sample to about 99.9% of the total nucleic acid in a sample.In some embodiments, a majority nucleic acid can have an abundance of atleast about 60% of the total nucleic acid in a sample to about 99% ofthe total nucleic acid in a sample. In some embodiments, a majoritynucleic acid can have an abundance of at least about 70% of the totalnucleic acid in a sample to about 98% of the total nucleic acid in asample. In some embodiments, a majority nucleic acid can have anabundance of at least about 75% of the total nucleic acid in a sample toabout 97% of the total nucleic acid in a sample. For example, a majoritynucleic acid can have an abundance of at least about 70%, 71%, 72%, 73%,74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of thetotal nucleic acid in a sample. In some embodiments, the majoritynucleic acid is extracellular DNA. In some embodiments, the majoritynucleic acid is extracellular maternal DNA. In some embodiments, themajority nucleic acid is DNA from healthy tissue. In some embodiments,the majority nucleic acid is DNA from non-tumor cells.

In some embodiments, a minority species of extracellular nucleic acid isof a length of about 500 base pairs or less (e.g., about 80, 85, 90, 91,92, 93, 94, 95, 96, 97, 98, 99 or 100% of minority species nucleic acidis of a length of about 500 base pairs or less). In some embodiments, aminority species of extracellular nucleic acid is of a length of about300 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96,97, 98, 99 or 100% of minority species nucleic acid is of a length ofabout 300 base pairs or less). In some embodiments, a minority speciesof extracellular nucleic acid is of a length of about 250 base pairs orless (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100%of minority species nucleic acid is of a length of about 250 base pairsor less). In some embodiments, a minority species of extracellularnucleic acid is of a length of about 200 base pairs or less (e.g., about80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of minorityspecies nucleic acid is of a length of about 200 base pairs or less). Insome embodiments, a minority species of extracellular nucleic acid is ofa length of about 150 base pairs or less (e.g., about 80, 85, 90, 91,92, 93, 94, 95, 96, 97, 98, 99 or 100% of minority species nucleic acidis of a length of about 150 base pairs or less). In some embodiments, aminority species of extracellular nucleic acid is of a length of about100 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96,97, 98, 99 or 100% of minority species nucleic acid is of a length ofabout 100 base pairs or less). In some embodiments, a minority speciesof extracellular nucleic acid is of a length of about 50 base pairs orless (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100%of minority species nucleic acid is of a length of about 50 base pairsor less).

Nucleic acid may be provided for conducting methods described hereinwith or without processing of the sample(s) containing the nucleic acid.In some embodiments, nucleic acid is provided for conducting methodsdescribed herein after processing of the sample(s) containing thenucleic acid. For example, a nucleic acid can be extracted, isolated,purified, partially purified or amplified from the sample(s). The term“isolated” as used herein refers to nucleic acid removed from itsoriginal environment (e.g., the natural environment if it is naturallyoccurring, or a host cell if expressed exogenously), and thus is alteredby human intervention (e.g., “by the hand of man”) from its originalenvironment. The term “isolated nucleic acid” as used herein can referto a nucleic acid removed from a subject (e.g., a human subject). Anisolated nucleic acid can be provided with fewer non-nucleic acidcomponents (e.g., protein, lipid) than the amount of components presentin a source sample. A composition comprising isolated nucleic acid canbe about 50% to greater than 99% free of non-nucleic acid components. Acomposition comprising isolated nucleic acid can be about 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99% free ofnon-nucleic acid components. The term “purified” as used herein canrefer to a nucleic acid provided that contains fewer non-nucleic acidcomponents (e.g., protein, lipid, carbohydrate) than the amount ofnon-nucleic acid components present prior to subjecting the nucleic acidto a purification procedure. A composition comprising purified nucleicacid may be about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99% free ofother non-nucleic acid components. The term “purified” as used hereincan refer to a nucleic acid provided that contains fewer nucleic acidspecies than in the sample source from which the nucleic acid isderived. A composition comprising purified nucleic acid may be about90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99%free of other nucleic acid species. For example, fetal nucleic acid canbe purified from a mixture comprising maternal and fetal nucleic acid.In certain examples, small fragments of fetal nucleic acid (e.g., 30 to500 bp fragments) can be purified, or partially purified, from a mixturecomprising both fetal and maternal nucleic acid templates. In certainexamples, nucleosomes comprising smaller fragments of fetal nucleic acidcan be purified from a mixture of larger nucleosome complexes comprisinglarger fragments of maternal nucleic acid. In certain examples, cancercell nucleic acid can be purified from a mixture comprising cancer celland non-cancer cell nucleic acid. In certain examples, nucleosomescomprising small fragments of cancer cell nucleic acid can be purifiedfrom a mixture of larger nucleosome complexes comprising largerfragments of non-cancer nucleic acid. In some embodiments, nucleic acidis provided for conducting methods described herein without priorprocessing of the sample(s) containing the nucleic acid. For example,nucleic acid may be analyzed directly from a sample without priorextraction, purification, partial purification, and/or amplification.

In some embodiments nucleic acids, such as, for example, cellularnucleic acids, are sheared or cleaved prior to, during or after a methoddescribed herein. The term “shearing” or “cleavage” generally refers toa procedure or conditions in which a nucleic acid molecule, such as anucleic acid template gene molecule or amplified product thereof, may besevered into two (or more) smaller nucleic acid molecules. Such shearingor cleavage can be sequence specific, base specific, or nonspecific, andcan be accomplished by any of a variety of methods, reagents orconditions, including, for example, chemical, enzymatic, physicalshearing (e.g., physical fragmentation). Sheared or cleaved nucleicacids may have a nominal, average or mean length of about 5 to about10,000 base pairs, about 100 to about 1,000 base pairs, about 100 toabout 500 base pairs, or about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400, 500, 600, 700, 800,900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000 or 9000 base pairs.

Sheared or cleaved nucleic acids can be generated by a suitable method,non-limiting examples of which include physical methods (e.g., shearing,e.g., sonication, French press, heat, UV irradiation, the like),enzymatic processes (e.g., enzymatic cleavage agents (e.g., a suitablenuclease, a suitable restriction enzyme, a suitable methylationsensitive restriction enzyme)), chemical methods (e.g., alkylation, DMS,piperidine, acid hydrolysis, base hydrolysis, heat, the like, orcombinations thereof), processes described in U.S. Patent ApplicationPublication No. 2005/0112590, the like or combinations thereof. Theaverage, mean or nominal length of the resulting nucleic acid fragmentscan be controlled by selecting an appropriate fragment-generatingmethod.

The term “amplified” as used herein refers to subjecting a nucleic acidin a sample to a process that linearly or exponentially generatesamplicon nucleic acids having the same or substantially the samenucleotide sequence as the nucleic acid, or part thereof. In certainembodiments the term “amplified” refers to a method that comprises apolymerase chain reaction (PCR). In certain instances, an amplifiedproduct can contain one or more nucleotides more than the amplifiednucleotide region of a nucleic acid template sequence (e.g., a primercan contain “extra” nucleotides such as a transcriptional initiationsequence, in addition to nucleotides complementary to a nucleic acidtemplate gene molecule, resulting in an amplified product containing“extra” nucleotides or nucleotides not corresponding to the amplifiednucleotide region of the nucleic acid template gene molecule).

Nucleic acid also may be exposed to a process that modifies certainnucleotides in the nucleic acid before providing nucleic acid for amethod described herein. A process that selectively modifies nucleicacid based upon the methylation state of nucleotides therein can beapplied to nucleic acid, for example. In addition, conditions such ashigh temperature, ultraviolet radiation, x-radiation, can induce changesin the sequence of a nucleic acid molecule. Nucleic acid may be providedin any suitable form useful for conducting a sequence analysis.

Enriching Nucleic Acids

In some embodiments, nucleic acid (e.g., extracellular nucleic acid) isenriched or relatively enriched for a subpopulation or species ofnucleic acid. Nucleic acid subpopulations can include, for example,fetal nucleic acid, maternal nucleic acid, cancer nucleic acid, patientnucleic acid, nucleic acid comprising templates of a particular lengthor range of lengths, or nucleic acid from a particular genome region(e.g., single chromosome, set of chromosomes, and/or certain chromosomeregions). Such enriched samples can be used in conjunction with a methodprovided herein. Thus, in certain embodiments, methods of the technologycomprise an additional step of enriching for a subpopulation of nucleicacid in a sample, such as, for example, cancer or fetal nucleic acid. Incertain embodiments, a method for determining fraction of cancer cellnucleic acid or fetal fraction also can be used to enrich for cancer orfetal nucleic acid. In certain embodiments, nucleic acid from normaltissue (e.g., non-cancer cells) is selectively removed (partially,substantially, almost completely or completely) from the sample. Incertain embodiments, maternal nucleic acid is selectively removed(partially, substantially, almost completely or completely) from thesample. In certain embodiments, enriching for a particular low copynumber species nucleic acid (e.g., cancer or fetal nucleic acid) mayimprove quantitative sensitivity. Methods for enriching a sample for aparticular species of nucleic acid are described, for example, in U.S.Pat. No. 6,927,028, International Patent Application Publication No.WO2007/140417, International Patent Application Publication No.WO2007/147063, International Patent Application Publication No.WO2009/032779, International Patent Application Publication No.WO2009/032781, International Patent Application Publication No.WO2010/033639, International Patent Application Publication No.WO2011/034631, International Patent Application Publication No.WO2006/056480, and International Patent Application Publication No.WO2011/143659, the entire content of each is incorporated herein byreference, including all text, tables, equations and drawings.

In some embodiments, nucleic acid is enriched for certain templatesand/or reference templates. In certain embodiments, nucleic acid isenriched for a specific nucleic acid template length or range oftemplate lengths using one or more length-based separation methodsdescribed below. In certain embodiments, nucleic acid is enriched fortemplates from a select genomic region (e.g., chromosome) using one ormore sequence-based separation methods described herein and/or known inthe art.

Non-limiting examples of methods for enriching for a nucleic acidsubpopulation in a sample include methods that exploit epigeneticdifferences between nucleic acid species (e.g., methylation-based fetalnucleic acid enrichment methods described in U.S. Patent ApplicationPublication No. 2010/0105049, which is incorporated by referenceherein); restriction endonuclease enhanced polymorphic sequenceapproaches (e.g., such as a method described in U.S. Patent ApplicationPublication No. 2009/0317818, which is incorporated by referenceherein); selective enzymatic degradation approaches; massively parallelsignature sequencing (MPSS) approaches; amplification (e.g., PCR)-basedapproaches (e.g., loci-specific amplification methods, multiplex SNPallele PCR approaches; universal amplification methods); pull-downapproaches (e.g., biotinylated ultramer pull-down methods); extensionand ligation-based methods (e.g., molecular inversion probe (MIP)extension and ligation); and combinations thereof.

In some embodiments, nucleic acid is enriched for templates from aselect genomic region (e.g., chromosome) using one or moresequence-based separation methods described herein. Sequence-basedseparation generally is based on nucleotide sequences present in thetemplates of interest (e.g., and/or target or reference templates) andsubstantially not present in other templates of the sample or present inan insubstantial amount of the other templates (e.g., 5% or less). Insome embodiments, sequence-based separation can generate separatedtarget templates and/or separated reference templates. Separated targettemplates and/or separated reference templates often are isolated awayfrom the remaining templates in the nucleic acid sample. In certainembodiments, the separated target templates and the separated referencetemplates also are isolated away from each other (e.g., isolated inseparate assay compartments). In certain embodiments, the separatedtarget templates and the separated reference templates are isolatedtogether (e.g., isolated in the same assay compartment). In someembodiments, unbound templates can be differentially removed or degradedor digested.

In some embodiments, a selective nucleic acid capture process is used toseparate target and/or reference templates away from a nucleic acidsample. Commercially available nucleic acid capture systems include, forexample, Nimblegen sequence capture system (Roche NimbleGen, Madison,Wis.); Illumina BEADARRAY platform (Illumina, San Diego, Calif.);Affymetrix GENECHIP platform (Affymetrix, Santa Clara, Calif.); AgilentSureSelect Target Enrichment System (Agilent Technologies, Santa Clara,Calif.); and related platforms. Such methods typically involvehybridization of a capture oligonucleotide to a part or all of thenucleotide sequence of a target or reference template and can includeuse of a solid phase (e.g., solid phase array) and/or a solution basedplatform. Capture oligonucleotides (sometimes referred to as “bait”) canbe selected or designed such that they preferentially hybridize tonucleic acid templates from selected genomic regions or loci (e.g., oneof chromosomes 21, 18, 13, X or Y, or a reference chromosome). Incertain embodiments, a hybridization-based method (e.g., usingoligonucleotide arrays) can be used to enrich for nucleic acid sequencesfrom certain chromosomes (e.g., a potentially aneuploid chromosome,reference chromosome or other chromosome of interest), genes or regionsof interest thereof Thus, in some embodiments, a nucleic acid sample isoptionally enriched by capturing a subset of templates using captureoligonucleotides complementary to, for example, selected genes in samplenucleic acid. In certain instances, captured templates are amplified.For example, captured templates containing adapters may be amplifiedusing primers complementary to the nonrandom oligonucleotide adapters toform collections of amplified templates, indexed according to adaptersequence. In some embodiments, nucleic acid is enriched for templatesfrom a select genomic region (e.g., chromosome, a gene) by amplificationof one or more regions of interest using oligonucleotides (e.g., PCRprimers) complementary to sequences in templates containing theregion(s) of interest, or part(s) thereof.

In some embodiments, nucleic acid is enriched for a particular nucleicacid template length, range of lengths, or lengths under or over aparticular threshold or cutoff using one or more length-based separationmethods. Nucleic acid template length typically refers to the number ofnucleotides in the template. Nucleic acid template length also issometimes referred to as nucleic acid template size. In someembodiments, a length-based separation method is performed withoutmeasuring lengths of individual templates. In some embodiments, a lengthbased separation method is performed in conjunction with a method fordetermining length of individual templates. In some embodiments,length-based separation refers to a size fractionation procedure whereall or part of the fractionated pool can be isolated (e.g., retained)and/or analyzed. Size fractionation procedures are known in the art(e.g., separation on an array, separation by a molecular sieve,separation by gel electrophoresis, separation by column chromatography(e.g., size-exclusion columns), and microfluidics-based approaches). Incertain instances, length-based separation approaches can includeselective sequence tagging approaches, fragment circularization,chemical treatment (e.g., formaldehyde, polyethylene glycol (PEG)precipitation), mass spectrometry and/or size-specific nucleic acidamplification, for example.

Nucleic Acid Quantification

The amount of nucleic acid (e.g., concentration, relative amount,absolute amount, copy number, and the like) in a sample may bedetermined. The amount of a minority nucleic acid (e.g., concentration,relative amount, absolute amount, copy number, and the like) in nucleicacid is determined in some embodiments. In certain embodiments, theamount of a minority nucleic acid species in a sample is referred to as“minority species fraction.” In some embodiments “minority speciesfraction” refers to the fraction of a minority nucleic acid species incirculating cell-free nucleic acid in a sample (e.g., a blood sample, aserum sample, a plasma sample, a urine sample) obtained from a subject.

The amount of a minority nucleic acid in extracellular nucleic acid canbe quantified and used in conjunction with a method provided herein.Thus, in certain embodiments, methods described herein comprise anadditional step of determining the amount of a minority nucleic acid.The amount of a minority nucleic acid can be determined in a sample froma subject before or after processing to prepare sample nucleic acid. Incertain embodiments, the amount of a minority nucleic acid is determinedin a sample after sample nucleic acid is processed and prepared, whichamount is utilized for further assessment. In some embodiments, anoutcome comprises factoring the minority species fraction in the samplenucleic acid (e.g., adjusting counts, removing samples, making a call ornot making a call).

A determination of minority species fraction can be performed before,during, or at any one point in a method described herein, or aftercertain methods described herein (e.g., detection of a genetic variationor genetic alteration). For example, to conduct a geneticvariation/genetic alteration determination method with a certainsensitivity or specificity, a minority nucleic acid quantificationmethod may be implemented prior to, during or after geneticvariation/genetic alteration determination to identify those sampleswith greater than about 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%,13%, 14%,15%,16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25% or moreminority nucleic acid. In some embodiments, samples determined as havinga certain threshold amount of minority nucleic acid (e.g., about 15% ormore minority nucleic acid; about 4% or more minority nucleic acid) arefurther analyzed for a genetic variation/genetic alteration, or thepresence or absence of a genetic variation/genetic alteration, forexample. In certain embodiments, determinations of, for example, agenetic variation or genetic alteration are selected (e.g., selected andcommunicated to a patient) only for samples having a certain thresholdamount of a minority nucleic acid (e.g., about 15% or more minoritynucleic acid; about 4% or more minority nucleic acid).

The amount of cancer cell nucleic acid (e.g., concentration, relativeamount, absolute amount, copy number, and the like) in nucleic acid isdetermined in some embodiments. In certain instances, the amount ofcancer cell nucleic acid in a sample is referred to as “fraction ofcancer cell nucleic acid,” and sometimes is referred to as “cancerfraction” or “tumor fraction.” In some embodiments “fraction of cancercell nucleic acid” refers to the fraction of cancer cell nucleic acid incirculating cell-free nucleic acid in a sample (e.g., a blood sample, aserum sample, a plasma sample, a urine sample) obtained from a subject.

The amount of fetal nucleic acid (e.g., concentration, relative amount,absolute amount, copy number, and the like) in nucleic acid isdetermined in some embodiments. In certain embodiments, the amount offetal nucleic acid in a sample is referred to as “fetal fraction.” Insome embodiments “fetal fraction” refers to the fraction of fetalnucleic acid in circulating cell-free nucleic acid in a sample (e.g., ablood sample, a serum sample, a plasma sample, a urine sample) obtainedfrom a pregnant female. Certain methods described herein or known in theart for determining fetal fraction can be used for determining afraction of cancer cell nucleic acid and/or a minority species fraction.

In certain instances, fetal fraction may be determined according tomarkers specific to a male fetus (e.g., Y-chromosome STR markers (e.g.,DYS 19, DYS 385, DYS 392 markers); RhD marker in RhD-negative females),allelic ratios of polymorphic sequences, or according to one or moremarkers specific to fetal nucleic acid and not maternal nucleic acid(e.g., differential epigenetic biomarkers (e.g., methylation) betweenmother and fetus, or fetal RNA markers in maternal blood plasma (seee.g., Lo, 2005, Journal of Histochemistry and Cytochemistry 53 (3):293-296)). Determination of fetal fraction sometimes is performed usinga fetal quantifier assay (FQA) as described, for example, in U.S. PatentApplication Publication No. 2010/0105049, which is hereby incorporatedby reference. This type of assay allows for the detection andquantification of fetal nucleic acid in a maternal sample based on themethylation status of the nucleic acid in the sample.

In certain embodiments, a minority species fraction can be determinedbased on allelic ratios of polymorphic sequences (e.g., singlenucleotide polymorphisms (SNPs)), such as, for example, using a methoddescribed in U.S. Patent Application Publication No. 2011/0224087, whichis hereby incorporated by reference. In such a method for determiningfetal fraction, for example, nucleotide sequence reads are obtained fora maternal sample and fetal fraction is determined by comparing thetotal number of nucleotide sequence reads that map to a first allele andthe total number of nucleotide sequence reads that map to a secondallele at an informative polymorphic site (e.g., SNP) in a referencegenome.

A minority species fraction can be determined, in some embodiments,using methods that incorporate information derived from chromosomalaberrations as described, for example, in International PatentApplication Publication No. WO2014/055774, which is incorporated byreference herein. A minority species fraction can be determined, in someembodiments, using methods that incorporate information derived from sexchromosomes as described, for example, in U.S. Patent ApplicationPublication No. 2013/0288244 and U.S. Patent Application Publication No.2013/0338933, each of which is incorporated by reference herein.

A minority species fraction can be determined in some embodiments usingmethods that incorporate fragment length information (e.g., fragmentlength ratio (FLR) analysis, fetal ratio statistic (FRS) analysis asdescribed in International Patent Application Publication No.WO2013/177086, which is incorporated by reference herein). Cell-freefetal nucleic acid fragments generally are shorter thanmaternally-derived nucleic acid fragments (see e.g., Chan et al. (2004)Clin. Chem. 50:88-92; Lo et al. (2010) Sci. Transl. Med. 2:61ra91).Thus, fetal fraction can be determined, in some embodiments, by countingtemplates under a particular length threshold and comparing the counts,for example, to counts from templates over a particular length thresholdand/or to the amount of total nucleic acid in the sample. Methods forcounting nucleic acid templates of a particular length are described infurther detail in International Patent Application Publication No.WO2013/177086.

A minority species fraction can be determined, in some embodiments,according to portion-specific fraction estimates (e.g., as described inInternational Patent Application Publication No. WO 2014/205401, whichis incorporated by reference herein). Without being limited to theory,the amount of reads from fetal CCF fragments (e.g., fragments of aparticular length, or range of lengths) often map with rangingfrequencies to portions (e.g., within the same sample, e.g., within thesame sequencing run). Also, without being limited to theory, certainportions, when compared among multiple samples, tend to have a similarrepresentation of reads from fetal CCF fragments (e.g., fragments of aparticular length, or range of lengths), and that the representationcorrelates with portion-specific fetal fractions (e.g., the relativeamount, percentage or ratio of CCF fragments originating from a fetus).Portion-specific fetal fraction estimates generally are determinedaccording to portion-specific parameters and their relation to fetalfraction.

In some embodiments, the determination of minority species fraction(e.g., fraction of cancer cell nucleic acid; fetal fraction) is notrequired or necessary for identifying the presence or absence of agenetic variation or genetic alteration. In some embodiments,identifying the presence or absence of a genetic variation or geneticalteration does not require a sequence differentiation of a minoritynucleic acid versus a majority nucleic acid. In certain embodiments,this is because the summed contribution of both minority and majoritysequences in a particular chromosome, chromosome portion or part thereofis analyzed. In some embodiments, identifying the presence or absence ofa genetic variation or genetic alteration does not rely on a priorisequence information that would distinguish minority nucleic acid frommajority nucleic acid.

Nucleic Acid Library

In some embodiments a nucleic acid library is a plurality ofpolynucleotide molecules (e.g., a sample of nucleic acids) that areprepared, assembled and/or modified for a specific process, non-limitingexamples of which include immobilization on a solid phase (e.g., a solidsupport, a flow cell, a bead), enrichment, amplification, cloning,detection and/or for nucleic acid sequencing. In certain embodiments, anucleic acid library is prepared prior to or during a sequencingprocess. A nucleic acid library (e.g., sequencing library) can beprepared by a suitable method as known in the art. A nucleic acidlibrary can be prepared by a targeted or a non-targeted preparationprocess.

In some embodiments a library of nucleic acids is modified to comprise achemical moiety (e.g., a functional group) configured for immobilizationof nucleic acids to a solid support. In some embodiments a library ofnucleic acids is modified to comprise a biomolecule (e.g., a functionalgroup) and/or member of a binding pair configured for immobilization ofthe library to a solid support, non-limiting examples of which includethyroxin-binding globulin, steroid-binding proteins, antibodies,antigens, haptens, enzymes, lectins, nucleic acids, repressors, proteinA, protein G, avidin, streptavidin, biotin, complement component C1q,nucleic acid-binding proteins, receptors, carbohydrates,oligonucleotides, polynucleotides, complementary nucleic acid sequences,the like and combinations thereof. Some examples of specific bindingpairs include, without limitation: an avidin moiety and a biotin moiety;an antigenic epitope and an antibody or immunologically reactivefragment thereof; an antibody and a hapten; a digoxigen moiety and ananti-digoxigen antibody; a fluorescein moiety and an anti-fluoresceinantibody; an operator and a repressor; a nuclease and a nucleotide; alectin and a polysaccharide; a steroid and a steroid-binding protein; anactive compound and an active compound receptor; a hormone and a hormonereceptor; an enzyme and a substrate; an immunoglobulin and protein A; anoligonucleotide or polynucleotide and its corresponding complement; thelike or combinations thereof.

In some embodiments, a library of nucleic acids is modified to compriseone or more polynucleotides of known composition, non-limiting examplesof which include an identifier (e.g., a tag, an indexing tag), a capturesequence, a label, an adapter, a restriction enzyme site, a promoter, anenhancer, an origin of replication, a stem loop, a complimentarysequence (e.g., a primer binding site, an annealing site), a suitableintegration site (e.g., a transposon, a viral integration site), amodified nucleotide, the like or combinations thereof. Polynucleotidesof known sequence can be added at a suitable position, for example onthe 5′ end, 3′ end or within a nucleic acid sequence. Polynucleotides ofknown sequence can be the same or different sequences. In someembodiments a polynucleotide of known sequence is configured tohybridize to one or more oligonucleotides immobilized on a surface(e.g., a surface in flow cell). For example, a nucleic acid moleculecomprising a 5′ known sequence may hybridize to a first plurality ofoligonucleotides while the 3′ known sequence may hybridize to a secondplurality of oligonucleotides. In some embodiments a library of nucleicacid can comprise chromosome-specific tags, capture sequences, labelsand/or adapters. In some embodiments, a library of nucleic acidscomprises one or more detectable labels. In some embodiments one or moredetectable labels may be incorporated into a nucleic acid library at a5′ end, at a 3′ end, and/or at any nucleotide position within a nucleicacid in the library. In some embodiments a library of nucleic acidscomprises hybridized oligonucleotides. In certain embodiments hybridizedoligonucleotides are labeled probes. In some embodiments a library ofnucleic acids comprises hybridized oligonucleotide probes prior toimmobilization on a solid phase.

In some embodiments, a polynucleotide of known sequence comprises auniversal sequence. A universal sequence is a specific nucleotidesequence that is integrated into two or more nucleic acid molecules ortwo or more subsets of nucleic acid molecules where the universalsequence is the same for all molecules or subsets of molecules that itis integrated into. A universal sequence is often designed to hybridizeto and/or amplify a plurality of different sequences using a singleuniversal primer that is complementary to a universal sequence. In someembodiments two (e.g., a pair) or more universal sequences and/oruniversal primers are used. A universal primer often comprises auniversal sequence. In some embodiments adapters (e.g., universaladapters) comprise universal sequences. In some embodiments one or moreuniversal sequences are used to capture, identify and/or detect multiplespecies or subsets of nucleic acids.

In certain embodiments of preparing a nucleic acid library, (e.g., incertain sequencing by synthesis procedures), nucleic acids are sizeselected and/or fragmented into lengths of several hundred base pairs,or less (e.g., in preparation for library generation). In someembodiments, library preparation is performed without fragmentation(e.g., when using cell-free DNA).

In certain embodiments, a ligation-based library preparation method isused (e.g., ILLUMINA TRUSEQ, Illumina, San Diego Calif.). Ligation-basedlibrary preparation methods often make use of an adapter (e.g., amethylated adapter) design which can incorporate an index sequence(e.g., a sample index sequence to identify sample origin for a nucleicacid sequence) at the initial ligation step and often can be used toprepare samples for single-read sequencing, paired-end sequencing andmultiplexed sequencing. For example, nucleic acids (e.g., fragmentednucleic acids or cell-free DNA) may be end repaired by a fill-inreaction, an exonuclease reaction or a combination thereof. In someembodiments the resulting blunt-end repaired nucleic acid can then beextended by a single nucleotide, which is complementary to a singlenucleotide overhang on the 3′ end of an adapter/primer. Any nucleotidecan be used for the extension/overhang nucleotides.

In some embodiments nucleic acid library preparation comprises ligatingan oligonucleotide adapter. Oligonucleotide adapters are oftencomplementary to flow-cell anchors, and sometimes are utilized toimmobilize a nucleic acid library to a solid support, such as the insidesurface of a flow cell, for example. In some embodiments, a nonrandomoligonucleotide adapter comprises an identifier, one or more sequencingprimer hybridization sites (e.g., sequences complementary to universalsequencing primers, single end sequencing primers, paired end sequencingprimers, multiplexed sequencing primers, and the like), or combinationsthereof (e.g., adapter/sequencing, adapter/identifier,adapter/identifier/sequencing). In some embodiments, a nonrandomoligonucleotide adapter comprises one or more of primer annealingpolynucleotide (e.g., for annealing to flow cell attachedoligonucleotides and/or to free amplification primers), an indexpolynucleotide (e.g., sample index sequence for tracking nucleic acidfrom different samples), and a barcode polynucleotide (e.g., singlemolecule barcode (SMB) or duplex barcode (DB) for tracking individualmolecules of sample nucleic acid that are amplified prior tosequencing).

An identifier can be a suitable detectable label incorporated into orattached to a nucleic acid (e.g., a polynucleotide) that allowsdetection and/or identification of nucleic acids that comprise theidentifier. In some embodiments an identifier is incorporated into orattached to a nucleic acid during a sequencing method (e.g., by apolymerase). Non-limiting examples of identifiers include nucleic acidtags, nucleic acid indexes or barcodes, a radiolabel (e.g., an isotope),metallic label, a fluorescent label, a chemiluminescent label, aphosphorescent label, a fluorophore quencher, a dye, a protein (e.g., anenzyme, an antibody or part thereof, a linker, a member of a bindingpair), the like or combinations thereof. In some embodiments anidentifier (e.g., a nucleic acid index or barcode) is a unique, knownand/or identifiable sequence of nucleotides or nucleotide analogues. Insome embodiments identifiers are six or more contiguous nucleotides. Amultitude of fluorophores are available with a variety of differentexcitation and emission spectra. Any suitable type and/or number offluorophores can be used as an identifier. In some embodiments 1 ormore, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more,8 or more, 9 or more, 10 or more, 20 or more, 30 or more or 50 or moredifferent identifiers are utilized in a method described herein (e.g., anucleic acid detection and/or sequencing method). In some embodiments,one or two types of identifiers (e.g., fluorescent labels) are linked toeach nucleic acid in a library. Detection and/or quantification of anidentifier can be performed by a suitable method, apparatus or machine,non-limiting examples of which include flow cytometry, quantitativepolymerase chain reaction (qPCR), gel electrophoresis, a luminometer, afluorometer, a spectrophotometer, a suitable gene-chip or microarrayanalysis, Western blot, mass spectrometry, chromatography,cytofluorimetric analysis, fluorescence microscopy, a suitablefluorescence or digital imaging method, confocal laser scanningmicroscopy, laser scanning cytometry, affinity chromatography, manualbatch mode separation, electric field suspension, a suitable nucleicacid sequencing method and/or nucleic acid sequencing apparatus, thelike and combinations thereof.

In some embodiments, a transposon-based library preparation method isused (e.g., EPICENTRE NEXTERA, Epicentre, Madison, Wis.).Transposon-based methods typically use in vitro transposition tosimultaneously fragment and tag DNA in a single-tube reaction (oftenallowing incorporation of platform-specific tags and optional barcodes),and prepare sequencer-ready libraries.

In some embodiments, a nucleic acid library or parts thereof areamplified (e.g., amplified by a PCR-based method). In some embodiments asequencing method comprises amplification of a nucleic acid library. Anucleic acid library can be amplified prior to or after immobilizationon a solid support (e.g., a solid support in a flow cell). Nucleic acidamplification includes the process of amplifying or increasing thenumbers of a nucleic acid template and/or of a complement thereof thatare present (e.g., in a nucleic acid library), by producing one or morecopies of the template and/or its complement. Amplification can becarried out by a suitable method. A nucleic acid library can beamplified by a thermocycling method or by an isothermal amplificationmethod. In some embodiments a rolling circle amplification method isused. In some embodiments amplification takes place on a solid support(e.g., within a flow cell) where a nucleic acid library or portionthereof is immobilized. In certain sequencing methods, a nucleic acidlibrary is added to a flow cell and immobilized by hybridization toanchors under suitable conditions. This type of nucleic acidamplification is often referred to as solid phase amplification. In someembodiments of solid phase amplification, all or a portion of theamplified products are synthesized by an extension initiating from animmobilized primer. Solid phase amplification reactions are analogous tostandard solution phase amplifications except that at least one of theamplification oligonucleotides (e.g., primers) is immobilized on a solidsupport. In some embodiments, modified nucleic acid (e.g., nucleic acidmodified by addition of adapters) is amplified.

In some embodiments, solid phase amplification comprises a nucleic acidamplification reaction comprising only one species of oligonucleotideprimer immobilized to a surface. In certain embodiments solid phaseamplification comprises a plurality of different immobilizedoligonucleotide primer species. In some embodiments solid phaseamplification may comprise a nucleic acid amplification reactioncomprising one species of oligonucleotide primer immobilized on a solidsurface and a second different oligonucleotide primer species insolution. Multiple different species of immobilized or solution basedprimers can be used. Non-limiting examples of solid phase nucleic acidamplification reactions include interfacial amplification, bridgeamplification, emulsion PCR, WildFire amplification (e.g., U.S. PatentApplication Publication No. 2013/0012399), the like or combinationsthereof.

An embodiment of nucleic acid library preparation is illustrated in FIG.4 . Sample nucleic acid 205 is subjected to adapter ligation andamplification to generate an adapter-ligated sample nucleic acid library215. One embodiment of adapter ligation and amplification is illustratedas process 211. Sample nucleic acid 205 is subjected to adapter ligation212 which generates adapter-ligated sample nucleic acid 213.Adapter-ligated sample nucleic acid 213 is subjected to amplification214 which generates an adapter-ligated sample nucleic acid library 215.

Nucleic Acid Capture

In some embodiments, a sample nucleic acid (or a sample nucleic acidlibrary) is subjected to a target capture process. Generally a targetcapture process is performed by contacting sample nucleic acid (or asample nucleic acid library) with a set of probe oligonucleotides underhybridization conditions. A set of probe oligonucleotides (e.g., captureoligonucleotides or capture probes) generally includes a plurality ofprobe oligonucleotides having sequences that are complementary to, orsubstantially complementary to, sequences in sample nucleic acid. Aplurality of probe oligonucleotides may include about 10 probeoligonucleotide species, about 50 probe oligonucleotide species, about100 probe oligonucleotide species, about 500 probe oligonucleotidespecies, about 1,000 probe oligonucleotide species, 2,000 probeoligonucleotide species, 3,000 probe oligonucleotide species, 4,000probe oligonucleotide species, 5000 probe oligonucleotide species,10,000 probe oligonucleotide species, or more. Generally, a first probeoligonucleotide species has a different nucleotide sequence than asecond probe oligonucleotide species, and different species of probeoligonucleotides in a set each have a different nucleotide sequence.

A probe oligonucleotide typically comprises a nucleotide sequencecapable of hybridizing or annealing to a nucleic acid template ofinterest (e.g. target template) or a portion thereof. A probeoligonucleotide may be naturally occurring or synthetic and may be DNAor RNA based. Probe oligonucleotides can allow for specific separationof, for example, a target template away from other templates in anucleic acid sample. The term “specific” or “specificity,” as usedherein, refers to the binding or hybridization of one molecule toanother molecule, such as an oligonucleotide for a targetpolynucleotide. “Specific” or “specificity” refers to the recognition,contact, and formation of a stable complex between two molecules, ascompared to substantially less recognition, contact, or complexformation of either of those two molecules with other molecules. As usedherein, the terms “anneal” and “hybridize” refer to the formation of astable complex between two molecules. The terms “probe,” probeoligonucleotide,” “capture probe,” “capture oligonucleotide,” “captureoligo,” “oligo,” or “oligonucleotide” may be used interchangeablythroughout the document, when referring to probe oligonucleotides.

A probe oligonucleotide can be designed and synthesized using a suitableprocess, and may be of any length suitable for hybridizing to anucleotide sequence of interest and performing separation and/oranalysis processes described herein. Oligonucleotides may be designedbased upon a nucleotide sequence of interest (e.g., target templatesequence, genomic sequence, gene sequence). An oligonucleotide (e.g., aprobe oligonucleotide), in some embodiments, may be about 10 to about300 nucleotides, about 50 to about 200 nucleotides, about 75 to about150 nucleotides, about 110 to about 130 nucleotides, or about 111, 112,113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,127, 128, or 129 nucleotides in length. An oligonucleotide may becomposed of naturally occurring and/or non-naturally occurringnucleotides (e.g., labeled nucleotides), or a mixture thereof.Oligonucleotides suitable for use with embodiments described herein, maybe synthesized and labeled using known techniques. Oligonucleotides maybe chemically synthesized according to the solid phase phosphoramiditetriester method first described by Beaucage and Caruthers (1981)Tetrahedron Letts. 22:1859-1862, using an automated synthesizer, and/oras described in Needham-VanDevanter et al. (1984) Nucleic Acids Res.12:6159-6168. Purification of oligonucleotides can be effected by nativeacrylamide gel electrophoresis or by anion-exchange high-performanceliquid chromatography (HPLC), for example, as described in Pearson andRegnier (1983) J. Chrom. 255:137-149.

All or a portion of a probe oligonucleotide sequence (naturallyoccurring or synthetic) may be substantially complementary to a targetsequence or portion thereof, in some embodiments. As referred to herein,“substantially complementary” with respect to sequences refers tonucleotide sequences that will hybridize with each other. The stringencyof the hybridization conditions can be altered to tolerate varyingamounts of sequence mismatch. Included are target and oligonucleotidesequences that are 55% or more, 56% or more, 57% or more, 58% or more,59% or more, 60% or more, 61% or more, 62% or more, 63% or more, 64% ormore, 65% or more, 66% or more, 67% or more, 68% or more, 69% or more,70% or more, 71% or more, 72% or more, 73% or more, 74% or more, 75% ormore, 76% or more, 77% or more, 78% or more, 79% or more, 80% or more,81% or more, 82% or more, 83% or more, 84% or more, 85% or more, 86% ormore, 87% or more, 88% or more, 89% or more, 90% or more, 91% or more,92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% ormore, 98% or more or 99% or more complementary to each other.

Probe oligonucleotides that are substantially complimentary to anucleotide sequence of interest (e.g., target sequence) or portionthereof are also substantially similar to the compliment of the targetsequence or relevant portion thereof (e.g., substantially similar to theanti-sense strand of the nucleic acid). One test for determining whethertwo nucleotide sequences are substantially similar is to determine thepercent of identical nucleotide sequences shared. As referred to herein,“substantially similar” with respect to sequences refers to nucleotidesequences that are 55% or more, 56% or more, 57% or more, 58% or more,59% or more, 60% or more, 61% or more, 62% or more, 63% or more, 64% ormore, 65% or more, 66% or more, 67% or more, 68% or more, 69% or more,70% or more, 71% or more, 72% or more, 73% or more, 74% or more, 75% ormore, 76% or more, 77% or more, 78% or more, 79% or more, 80% or more,81% or more, 82% or more, 83% or more, 84% or more, 85% or more, 86% ormore, 87% or more, 88% or more, 89% or more, 90% or more, 91% or more,92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% ormore, 98% or more or 99% or more identical to each other.

Hybridization conditions (e.g., annealing conditions) can be determinedand/or adjusted, depending on the characteristics of theoligonucleotides used in an assay. Oligonucleotide sequence and/orlength sometimes may affect hybridization to a nucleic acid sequence ofinterest. Depending on the degree of mismatch between an oligonucleotideand nucleic acid of interest, low, medium or high stringency conditionsmay be used to effect the annealing. As used herein, the term “stringentconditions” refers to conditions for hybridization and washing. Methodsfor hybridization reaction temperature condition optimization are knownin the art, and may be found in Current Protocols in Molecular Biology,John Wiley & Sons, N.Y., 6.3.1-6.3.6 (1989). Aqueous and non-aqueousmethods are described in that reference and either can be used.Non-limiting examples of stringent hybridization conditions arehybridization in 6× sodium chloride/sodium citrate (SSC) at about 45°C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 50° C.Another example of stringent hybridization conditions are hybridizationin 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed byone or more washes in 0.2×SSC, 0.1% SDS at 55° C. A further example ofstringent hybridization conditions is hybridization in 6× sodiumchloride/sodium citrate (SSC) at about 45° C., followed by one or morewashes in 0.2×SSC, 0.1% SDS at 60° C. Often, stringent hybridizationconditions are hybridization in 6× sodium chloride/sodium citrate (SSC)at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at65° C. More often, stringency conditions are 0.5M sodium phosphate, 7%SDS at 65° C., followed by one or more washes at 0.2×SSC, 1% SDS at 65°C. Stringent hybridization temperatures can also be altered (i.e.lowered) with the addition of certain organic solvents, formamide forexample. Organic solvents, like formamide, reduce the thermal stabilityof double-stranded polynucleotides, so that hybridization can beperformed at lower temperatures, while still maintaining stringentconditions and extending the useful life of nucleic acids that may beheat labile.

In some embodiments, one or more probe oligonucleotides are associatedwith an affinity ligand such as a member of a binding pair (e.g.,biotin) or antigen that can bind to a capture agent such as avidin,streptavidin, an antibody, or a receptor. For example, a probeoligonucleotide may be biotinylated such that it can be captured onto astreptavidin-coated bead.

In some embodiments, one or more probe oligonucleotides and/or captureagents are effectively linked to a solid support or substrate. A solidsupport or substrate can be any physically separable solid to which aprobe oligonucleotide can be directly or indirectly attached including,but not limited to, surfaces provided by microarrays and wells, andparticles such as beads (e.g., paramagnetic beads, magnetic beads,microbeads, nanobeads), microparticles, and nanoparticles. Solidsupports also can include, for example, chips, columns, optical fibers,wipes, filters (e.g., flat surface filters), one or more capillaries,glass and modified or functionalized glass (e.g., controlled-pore glass(CPG)), quartz, mica, diazotized membranes (paper or nylon),polyformaldehyde, cellulose, cellulose acetate, paper, ceramics, metals,metalloids, semiconductive materials, quantum dots, coated beads orparticles, other chromatographic materials, magnetic particles; plastics(including acrylics, polystyrene, copolymers of styrene or othermaterials, polybutylene, polyurethanes, TEFLON™, polyethylene,polypropylene, polyamide, polyester, polyvinylidenedifluoride (PVDF),and the like), polysaccharides, nylon or nitrocellulose, resins, silicaor silica-based materials including silicon, silica gel, and modifiedsilicon, Sephadex®, Sepharose®, carbon, metals (e.g., steel, gold,silver, aluminum, silicon and copper), inorganic glasses, conductingpolymers (including polymers such as polypyrole and polyindole); microor nanostructured surfaces such as nucleic acid tiling arrays, nanotube,nanowire, or nanoparticulate decorated surfaces; or porous surfaces orgels such as methacrylates, acrylamides, sugar polymers, cellulose,silicates, or other fibrous or stranded polymers. In some embodiments,the solid support or substrate may be coated using passive orchemically-derivatized coatings with any number of materials, includingpolymers, such as dextrans, acrylamides, gelatins or agarose. Beadsand/or particles may be free or in connection with one another (e.g.,sintered). In some embodiments, the solid phase can be a collection ofparticles. In some embodiments, the particles can comprise silica, andthe silica may comprise silica dioxide. In some embodiments the silicacan be porous, and in certain embodiments the silica can be non-porous.In some embodiments, the particles further comprise an agent thatconfers a paramagnetic property to the particles. In certainembodiments, the agent comprises a metal, and in certain embodiments theagent is a metal oxide, (e.g., iron or iron oxides, where the iron oxidecontains a mixture of Fe2+ and Fe3+). The probe oligonucleotides may belinked to the solid support by covalent bonds or by non-covalentinteractions and may be linked to the solid support directly orindirectly (e.g., via an intermediary agent such as a spacer molecule orbiotin). A probe oligonucleotide may be linked to the solid supportbefore, during or after nucleic acid capture.

Nucleic acid that has been modified, such as modified by the addition ofadapter sequences described herein, may be captured. In someembodiments, unmodified nucleic acid is captured. Nucleic acid may beamplified before and/or after capture, in some embodiments, by anamplification process such as PCR. The term “captured nucleic acid”generally includes nucleic acid that has been captured and includesnucleic acid that has been captured and amplified. Captured nucleic acidmay be subjected to additional rounds of capture and amplification, insome embodiments. Captured nucleic acid may be sequenced, such as by asequencing process described herein.

An embodiment of a nucleic acid target capture process is illustrated inFIG. 2 . Sample nucleic acid 205 is subjected a nucleic acid captureprocess which generates probe-captured nucleic acid sequence reads 240.One embodiment of a nucleic acid capture process is illustrated asprocess 200. Sample nucleic acid 205 is subjected to probe hybridization220 which generates probe-captured sample nucleic acid 225.Probe-captured sample nucleic acid 225 is subjected to nucleic acidsequencing 230 which generates probe-captured nucleic acid sequencereads 240.

In some embodiments, a nucleic acid target capture process comprisesprobe hybridization to an adapter-ligated sample nucleic acid library.An embodiment of a nucleic acid target capture process comprising probehybridization to an adapter-ligated sample nucleic acid library isillustrated in FIG. 3 . Sample nucleic acid 205 is subjected to librarypreparation and adapter ligation 210 which generates an adapter-ligatedsample nucleic acid library 215. Adapter-ligated sample nucleic acidlibrary 215 is input for a nucleic acid capture process andprobe-captured nucleic acid reads 240 are generated. One embodiment of anucleic acid capture process is illustrated as process 200. Anadapter-ligated sample nucleic acid library 215 is subjected to probehybridization 220 which generates probe-captured sample nucleic acid225. Probe-captured sample nucleic acid, or “captured target nucleicacid” 225 is subjected to nucleic acid sequencing 230 which generatesprobe-captured nucleic acid sequence reads 240.

Nucleic Acid Sequencing and Processing

Methods provided herein generally include nucleic acid sequencing andanalysis. In some embodiments, nucleic acid is sequenced and thesequencing product (e.g., a collection of sequence reads) is processedprior to, or in conjunction with, an analysis of the sequenced nucleicacid. For example, sequence reads may be processed according to one ormore of the following: aligning, mapping, filtering portions, selectingportions, counting, normalizing, weighting, generating a profile, andthe like, and combinations thereof. Certain processing steps may beperformed in any order and certain processing steps may be repeated. Forexample, portions may be filtered followed by sequence read countnormalization, and, in certain embodiments, sequence read counts may benormalized followed by portion filtering. In some embodiments, a portionfiltering step is followed by sequence read count normalization followedby a further portion filtering step. Certain sequencing methods andprocessing steps are described in further detail below.

Sequencing

In some embodiments, nucleic acid (e.g., nucleic acid fragments, samplenucleic acid, cell-free nucleic acid) is sequenced. In certaininstances, a full or substantially full sequence is obtained andsometimes a partial sequence is obtained. Nucleic acid sequencinggenerally produces a collection of sequence reads.

As used herein, “reads” (e.g., “a read,” “a sequence read”) are shortnucleotide sequences produced by any sequencing process described hereinor known in the art. Reads can be generated from one end of nucleic acidfragments (“single-end reads”), and sometimes are generated from bothends of nucleic acid fragments (e.g., paired-end reads, double-endreads).

The length of a sequence read is often associated with the particularsequencing technology. High-throughput methods, for example, providesequence reads that can vary in size from tens to hundreds of base pairs(bp). Nanopore sequencing, for example, can provide sequence reads thatcan vary in size from tens to hundreds to thousands of base pairs. Insome embodiments, sequence reads are of a mean, median, average orabsolute length of about 15 bp to about 900 bp long. In certainembodiments sequence reads are of a mean, median, average or absolutelength of about 1000 bp or more. In some embodiments sequence reads areof a mean, median, average or absolute length of about 1500, 2000, 2500,3000, 3500, 4000, 4500, or 5000 bp or more. In some embodiments,sequence reads are of a mean, median, average or absolute length ofabout 100 bp to about 200 bp. In some embodiments, sequence reads are ofa mean, median, average or absolute length of about 140 bp to about 160bp. For example, sequence reads may be of a mean, median, average orabsolute length of about 140, 141, 142, 143, 144, 145, 146, 147, 148,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159 or 160 bp.

In some embodiments the nominal, average, mean or absolute length ofsingle-end reads sometimes is about 10 continuous nucleotides to about250 or more contiguous nucleotides, about 15 contiguous nucleotides toabout 200 or more contiguous nucleotides, about 15 contiguousnucleotides to about 150 or more contiguous nucleotides, about 15contiguous nucleotides to about 125 or more contiguous nucleotides,about 15 contiguous nucleotides to about 100 or more contiguousnucleotides, about 15 contiguous nucleotides to about 75 or morecontiguous nucleotides, about 15 contiguous nucleotides to about 60 ormore contiguous nucleotides, 15 contiguous nucleotides to about 50 ormore contiguous nucleotides, about 15 contiguous nucleotides to about 40or more contiguous nucleotides, and sometimes about 15 contiguousnucleotides or about 36 or more contiguous nucleotides. In certainembodiments the nominal, average, mean or absolute length of single-endreads is about 20 to about 30 bases, or about 24 to about 28 bases inlength. In certain embodiments the nominal, average, mean or absolutelength of single-end reads is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28 or about29 bases or more in length. In certain embodiments the nominal, average,mean or absolute length of single-end reads is about 20 to about 200bases, about 100 to about 200 bases, or about 140 to about 160 to about28 bases in length. In certain embodiments the nominal, average, mean orabsolute length of single-end reads is about 30, 40, 50, 60, 70, 80, 90,100, 110, 120, 130, 140, 150, 160, 170, 180, 190, or about 200 bases ormore in length. In certain embodiments, the nominal, average, mean orabsolute length of paired-end reads sometimes is about 10 contiguousnucleotides to about 25 contiguous nucleotides or more (e.g., about 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25 nucleotidesin length or more), about 15 contiguous nucleotides to about 20contiguous nucleotides or more, and sometimes is about 17 contiguousnucleotides or about 18 contiguous nucleotides. In certain embodiments,the nominal, average, mean or absolute length of paired-end readssometimes is about 25 contiguous nucleotides to about 400 contiguousnucleotides or more (e.g., about 25, 30, 40, 50, 60, 70, 80, 90, 100,110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240,250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380,390, or 400 nucleotides in length or more), about 50 contiguousnucleotides to about 350 contiguous nucleotides or more, about 100contiguous nucleotides to about 325 contiguous nucleotides, about 150contiguous nucleotides to about 325 contiguous nucleotides, about 200contiguous nucleotides to about 325 contiguous nucleotides, 275contiguous nucleotides to about 310 contiguous nucleotides, about 100 toabout 200 contiguous nucleotides, about 100 to about 175 contiguousnucleotides, about 125 to about 175 contiguous nucleotides, andsometimes is about 140 to about 160 contiguous nucleotides. In certainembodiments, the nominal, average, mean, or absolute length ofpaired-end reads is about 150 contiguous nucleotides, and sometimes is150 contiguous nucleotides.

In some embodiments, nucleotide sequence reads obtained from a sampleare partial nucleotide sequence reads. As used herein, “partialnucleotide sequence reads” refers to sequence reads of any length withincomplete sequence information, also referred to as sequence ambiguity.Partial nucleotide sequence reads may lack information regardingnucleobase identity and/or nucleobase position or order. Partialnucleotide sequence reads generally do not include sequence reads inwhich the only incomplete sequence information (or in which less thanall of the bases are sequenced or determined) is from inadvertent orunintentional sequencing errors. Such sequencing errors can be inherentto certain sequencing processes and include, for example, incorrectcalls for nucleobase identity, and missing or extra nucleobases. Thus,for partial nucleotide sequence reads herein, certain information aboutthe sequence is often deliberately excluded. That is, one deliberatelyobtains sequence information with respect to less than all of thenucleobases or which might otherwise be characterized as or be asequencing error. In some embodiments, a partial nucleotide sequenceread can span a portion of a nucleic acid template. In some embodiments,a partial nucleotide sequence read can span the entire length of anucleic acid template. Partial nucleotide sequence reads are described,for example, in International Patent Application Publication No.WO2013/052907, the entire content of which is incorporated herein byreference, including all text, tables, equations and drawings.

Reads generally are representations of nucleotide sequences in aphysical nucleic acid. For example, in a read containing an ATGCdepiction of a sequence, “A” represents an adenine nucleotide, “T”represents a thymine nucleotide, “G” represents a guanine nucleotide and“C” represents a cytosine nucleotide, in a physical nucleic acid.Sequence reads obtained from a sample from a subject can be reads from amixture of a minority nucleic acid and a majority nucleic acid. Forexample, sequence reads obtained from the blood of a cancer patient canbe reads from a mixture of cancer nucleic acid and non-cancer nucleicacid. In another example, sequence reads obtained from the blood of apregnant female can be reads from a mixture of fetal nucleic acid andmaternal nucleic acid. A mixture of relatively short reads can betransformed by processes described herein into a representation ofgenomic nucleic acid present in the subject, and/or a representation ofgenomic nucleic acid present in a tumor or a fetus. In certaininstances, a mixture of relatively short reads can be transformed into arepresentation of a copy number alteration, a genetic variation/geneticalteration or an aneuploidy, for example. In one example, reads of amixture of cancer and non-cancer nucleic acid can be transformed into arepresentation of a composite chromosome or a part thereof comprisingfeatures of one or both cancer cell and non-cancer cell chromosomes. Inanother example, reads of a mixture of maternal and fetal nucleic acidcan be transformed into a representation of a composite chromosome or apart thereof comprising features of one or both maternal and fetalchromosomes.

In some instances, circulating cell free nucleic acid fragments (CCFfragments) obtained from a cancer patient comprise nucleic acidfragments originating from normal cells (i.e., non-cancer fragments) andnucleic acid fragments originating from cancer cells (i.e., cancerfragments). Sequence reads derived from CCF fragments originating fromnormal cells (i.e., non-cancerous cells) are referred to herein as“non-cancer reads.” Sequence reads derived from CCF fragmentsoriginating from cancer cells are referred to herein as “cancer reads.”CCF fragments from which non-cancer reads are obtained may be referredto herein as non-cancer templates and CCF fragments from which cancerreads are obtained may be referred herein to as cancer templates.

In some instances, circulating cell free nucleic acid fragments (CCFfragments) obtained from a pregnant female comprise nucleic acidfragments originating from fetal cells (i.e., fetal fragments) andnucleic acid fragments originating from maternal cells (i.e., maternalfragments). Sequence reads derived from CCF fragments originating from afetus are referred to herein as “fetal reads.” Sequence reads derivedfrom CCF fragments originating from the genome of a pregnant female(e.g., a mother) bearing a fetus are referred to herein as “maternalreads.” CCF fragments from which fetal reads are obtained are referredto herein as fetal templates and CCF fragments from which maternal readsare obtained are referred herein to as maternal templates.

In certain embodiments, “obtaining” nucleic acid sequence reads of asample from a subject and/or “obtaining” nucleic acid sequence reads ofa biological specimen from one or more reference persons can involvedirectly sequencing nucleic acid to obtain the sequence information. Insome embodiments, “obtaining” can involve receiving sequence informationobtained directly from a nucleic acid by another.

In some embodiments, some or all nucleic acids in a sample are enrichedand/or amplified (e.g., non-specifically, e.g., by a PCR based method)prior to or during sequencing. In certain embodiments specific nucleicacid species or subsets in a sample are enriched and/or amplified priorto or during sequencing. In some embodiments, a species or subset of apre-selected pool of nucleic acids is sequenced randomly. In someembodiments, nucleic acids in a sample are not enriched and/or amplifiedprior to or during sequencing.

In some embodiments, a representative fraction of a genome is sequencedand is sometimes referred to as “coverage” or “fold coverage.” Forexample, a 1-fold coverage indicates that roughly 100% of the nucleotidesequences of the genome are represented by reads. In some instances,fold coverage is referred to as (and is directly proportional to)“sequencing depth.” In some embodiments, “fold coverage” is a relativeterm referring to a prior sequencing run as a reference. For example, asecond sequencing run may have 2-fold less coverage than a firstsequencing run. In some embodiments a genome is sequenced withredundancy, where a given region of the genome can be covered by two ormore reads or overlapping reads (e.g., a “fold coverage” greater than 1,e.g., a 2-fold coverage). In some embodiments, a genome (e.g., a wholegenome) is sequenced with about 0.01-fold to about 100-fold coverage,about 0.1-fold to 20-fold coverage, or about 0.1-fold to about 1-foldcoverage (e.g., about 0.015-, 0.02-, 0.03-, 0.04-, 0.05-, 0.06-, 0.07-,0.08-, 0.09-, 0.1-, 0.2-, 0.3-, 0.4-, 0.5-, 0.6-, 0.7-, 0.8-, 0.9-, 1-,2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, 10-, 15-, 20-, 30-, 40-, 50-, 60-, 70-,80-, 90-fold or greater coverage). In some embodiments, specific partsof a genome (e.g., genomic parts from targeted and/or probe-basedmethods) are sequenced and fold coverage values generally refer to thefraction of the specific genomic parts sequenced (i.e., fold coveragevalues do not refer to the whole genome). In some instances, specificgenomic parts are sequenced at 1000-fold coverage or more. For example,specific genomic parts may be sequenced at 2000-fold, 5,000-fold,10,000-fold, 20,000-fold, 30,000-fold, 40,000-fold or 50,000-foldcoverage. In some embodiments, sequencing is at about 100 fold to about200,000 fold coverage. In some embodiments, sequencing is at about 500fold to about 150,000 fold coverage. In some embodiments, sequencing isat about 1,000-fold to about 100,000-fold coverage. In some embodiments,sequencing is at about 10,000-fold to about 70,000-fold coverage. Insome embodiments, sequencing is at about 20,000-fold to about60,000-fold coverage. In some embodiments, sequencing is at about30,000-fold to about 50,000-fold coverage.

In some embodiments, one nucleic acid sample from one individual issequenced. In certain embodiments, nucleic acids from each of two ormore samples are sequenced, where samples are from one individual orfrom different individuals. In certain embodiments, nucleic acid samplesfrom two or more biological samples are pooled, where each biologicalsample is from one individual or two or more individuals, and the poolis sequenced. In the latter embodiments, a nucleic acid sample from eachbiological sample often is identified by one or more unique identifiers.

In some embodiments, a sequencing method utilizes identifiers that allowmultiplexing of sequence reactions in a sequencing process. The greaterthe number of unique identifiers, the greater the number of samplesand/or chromosomes for detection, for example, that can be multiplexedin a sequencing process. A sequencing process can be performed using anysuitable number of unique identifiers (e.g., 4, 8, 12, 24, 48, 96, ormore).

A sequencing process sometimes makes use of a solid phase, and sometimesthe solid phase comprises a flow cell on which nucleic acid from alibrary can be attached and reagents can be flowed and contacted withthe attached nucleic acid. A flow cell sometimes includes flow celllanes, and use of identifiers can facilitate analyzing a number ofsamples in each lane. A flow cell often is a solid support that can beconfigured to retain and/or allow the orderly passage of reagentsolutions over bound analytes. Flow cells frequently are planar inshape, optically transparent, generally in the millimeter orsub-millimeter scale, and often have channels or lanes in which theanalyte/reagent interaction occurs. In some embodiments the number ofsamples analyzed in a given flow cell lane is dependent on the number ofunique identifiers utilized during library preparation and/or probedesign. Multiplexing using 12 identifiers, for example, allowssimultaneous analysis of 96 samples (e.g., equal to the number of wellsin a 96 well microwell plate) in an 8 lane flow cell. Similarly,multiplexing using 48 identifiers, for example, allows simultaneousanalysis of 384 samples (e.g., equal to the number of wells in a 384well microwell plate) in an 8 lane flow cell. Non-limiting examples ofcommercially available multiplex sequencing kits include Illumina'smultiplexing sample preparation oligonucleotide kit and multiplexingsequencing primers and PhiX control kit (e.g., Illumina's catalognumbers PE-400-1001 and PE-400-1002, respectively).

Any suitable method of sequencing nucleic acids can be used,non-limiting examples of which include Maxim & Gilbert,chain-termination methods, sequencing by synthesis, sequencing byligation, sequencing by mass spectrometry, microscopy-based techniques,the like or combinations thereof. In some embodiments, a firstgeneration technology, such as, for example, Sanger sequencing methodsincluding automated Sanger sequencing methods, including microfluidicSanger sequencing, can be used in a method provided herein. In someembodiments, sequencing technologies that include the use of nucleicacid imaging technologies (e.g., transmission electron microscopy (TEM)and atomic force microscopy (AFM)), can be used. In some embodiments, ahigh-throughput sequencing method is used. High-throughput sequencingmethods generally involve clonally amplified DNA templates or single DNAmolecules that are sequenced in a massively parallel fashion, sometimeswithin a flow cell. Next generation (e.g., 2nd and 3rd generation)sequencing techniques capable of sequencing DNA in a massively parallelfashion can be used for methods described herein and are collectivelyreferred to herein as “massively parallel sequencing” (MPS). In someembodiments, MPS sequencing methods utilize a targeted approach, wherespecific chromosomes, genes or regions of interest are sequenced. Incertain embodiments, a non-targeted approach is used where most or allnucleic acids in a sample are sequenced, amplified and/or capturedrandomly.

In some embodiments a targeted enrichment, amplification and/orsequencing approach is used. A targeted approach often isolates, selectsand/or enriches a subset of nucleic acids in a sample for furtherprocessing by use of sequence-specific oligonucleotides. In someembodiments a library of sequence-specific oligonucleotides are utilizedto target (e.g., hybridize to) one or more sets of nucleic acids in asample. Sequence-specific oligonucleotides and/or primers are oftenselective for particular sequences (e.g., unique nucleic acid sequences)present in one or more chromosomes, genes, exons, introns, and/orregulatory regions of interest. Any suitable method or combination ofmethods can be used for enrichment, amplification and/or sequencing ofone or more subsets of targeted nucleic acids. In some embodimentstargeted sequences are isolated and/or enriched by capture to a solidphase (e.g., a flow cell, a bead) using one or more sequence-specificanchors. In some embodiments targeted sequences are enriched and/oramplified by a polymerase-based method (e.g., a PCR-based method, by anysuitable polymerase based extension) using sequence-specific primersand/or primer sets. Sequence specific anchors often can be used assequence-specific primers.

MPS sequencing sometimes makes use of sequencing by synthesis andcertain imaging processes. A nucleic acid sequencing technology that maybe used in a method described herein is sequencing-by-synthesis andreversible terminator-based sequencing (e.g., Illumina's GenomeAnalyzer; Genome Analyzer II; HISEQ 2000; HISEQ 2500 (Illumina, SanDiego Calif.)). With this technology, millions of nucleic acid (e.g.,DNA) templates can be sequenced in parallel. In one example of this typeof sequencing technology, a flow cell is used which contains anoptically transparent slide with 8 individual lanes on the surfaces ofwhich are bound oligonucleotide anchors (e.g., adapter primers).

Sequencing by synthesis generally is performed by iteratively adding(e.g., by covalent addition) a nucleotide to a primer or preexistingnucleic acid strand in a template directed manner. Each iterativeaddition of a nucleotide is detected and the process is repeatedmultiple times until a sequence of a nucleic acid strand is obtained.The length of a sequence obtained depends, in part, on the number ofaddition and detection steps that are performed. In some embodiments ofsequencing by synthesis, one, two, three or more nucleotides of the sametype (e.g., A, G, C or T) are added and detected in a round ofnucleotide addition. Nucleotides can be added by any suitable method(e.g., enzymatically or chemically). For example, in some embodiments apolymerase or a ligase adds a nucleotide to a primer or to a preexistingnucleic acid strand in a template directed manner. In some embodimentsof sequencing by synthesis, different types of nucleotides, nucleotideanalogues and/or identifiers are used. In some embodiments reversibleterminators and/or removable (e.g., cleavable) identifiers are used. Insome embodiments fluorescent labeled nucleotides and/or nucleotideanalogues are used. In certain embodiments sequencing by synthesiscomprises a cleavage (e.g., cleavage and removal of an identifier)and/or a washing step. In some embodiments the addition of one or morenucleotides is detected by a suitable method described herein or knownin the art, non-limiting examples of which include any suitable imagingapparatus, a suitable camera, a digital camera, a CCD (Charge CoupleDevice) based imaging apparatus (e.g., a CCD camera), a CMOS(Complementary Metal Oxide Silicon) based imaging apparatus (e.g., aCMOS camera), a photo diode (e.g., a photomultiplier tube), electronmicroscopy, a field-effect transistor (e.g., a DNA field-effecttransistor), an ISFET ion sensor (e.g., a CHEMFET sensor), the like orcombinations thereof.

Any suitable MPS method, system or technology platform for conductingmethods described herein can be used to obtain nucleic acid sequencereads. Non-limiting examples of MPS platforms includeIllumina/Sole,dHiSeq (e.g., Illumina's Genome Analyzer; Genome AnalyzerII; HISEQ 2000; HISEQ), SOLiD, Roche/454, PACBIO and/or SMRT, HelicosTrue Single Molecule Sequencing, Ion Torrent and Ion semiconductor-basedsequencing (e.g., as developed by Life Technologies), WildFire, 5500,5500xl W and/or 5500xl W Genetic Analyzer based technologies (e.g., asdeveloped and sold by Life Technologies, U.S. Patent ApplicationPublication No. 2013/0012399); Polony sequencing, Pyrosequencing,Massively Parallel Signature Sequencing (MPSS), RNA polymerase (RNAP)sequencing, LaserGen systems and methods, Nanopore-based platforms,chemical-sensitive field effect transistor (CHEMFET) array, electronmicroscopy-based sequencing (e.g., as developed by ZS Genetics, HalcyonMolecular), nanoball sequencing, the like or combinations thereof. Othersequencing methods that may be used to conduct methods herein includedigital PCR, sequencing by hybridization, nanopore sequencing,chromosome-specific sequencing (e.g., using DANSR (digital analysis ofselected regions) technology.

In some embodiments, sequence reads are generated, obtained, gathered,assembled, manipulated, transformed, processed, and/or provided by asequence module. A machine comprising a sequence module can be asuitable machine and/or apparatus that determines the sequence of anucleic acid utilizing a sequencing technology known in the art. In someembodiments a sequence module can align, assemble, fragment, complement,reverse complement, and/or error check (e.g., error correct sequencereads).

Mapping Reads

Sequence reads can be mapped and the number of reads mapping to aspecified nucleic acid region (e.g., a chromosome or portion thereof)are referred to as counts. Any suitable mapping method (e.g., process,algorithm, program, software, module, the like or combination thereof)can be used. Certain aspects of mapping processes are describedhereafter.

Mapping nucleotide sequence reads (i.e., sequence information from atemplate whose physical genomic position is unknown) can be performed ina number of ways, and often comprises alignment of the obtained sequencereads with a matching sequence in a reference genome. In suchalignments, sequence reads generally are aligned to a reference sequenceand those that align are designated as being “mapped,” as “a mappedsequence read” or as “a mapped read.” In certain embodiments, a mappedsequence read is referred to as a “hit” or “count.” In some embodiments,mapped sequence reads are grouped together according to variousparameters and assigned to particular genomic portions, which arediscussed in further detail below.

The terms “aligned,” “alignment,” or “aligning” generally refer to twoor more nucleic acid sequences that can be identified as a match (e.g.,100% identity) or partial match. Alignments can be done manually or by acomputer (e.g., a software, program, module, or algorithm), non-limitingexamples of which include the Efficient Local Alignment of NucleotideData (ELAND) computer program distributed as part of the IlluminaGenomics Analysis pipeline. Alignment of a sequence read can be a 100%sequence match. In some cases, an alignment is less than a 100% sequencematch (i.e., non-perfect match, partial match, partial alignment). Insome embodiments an alignment is about a 99%, 98%, 97%, 96%, 95%, 94%,93%, 92%, 91%, 90%, 89%, 88%, 87%, 86%, 85%, 84%, 83%, 82%, 81%, 80%,79%, 78%, 77%, 76% or 75% match. In some embodiments, an alignmentcomprises a mismatch. In some embodiments, an alignment comprises 1, 2,3, 4 or 5 mismatches. Two or more sequences can be aligned using eitherstrand (e.g., sense or antisense strand). In certain embodiments anucleic acid sequence is aligned with the reverse complement of anothernucleic acid sequence.

Various computational methods can be used to map each sequence read to aportion. Non-limiting examples of computer algorithms that can be usedto align sequences include, without limitation, BLAST, BLITZ, FASTA,BOWTIE 1, BOWTIE 2, ELAND, MAQ, PROBEMATCH, SOAP, BWA or SEQMAP, orvariations thereof or combinations thereof. In some embodiments,sequence reads can be aligned with sequences in a reference genome. Insome embodiments, sequence reads can be found and/or aligned withsequences in nucleic acid databases known in the art including, forexample, GenBank, dbEST, dbSTS, EMBL (European Molecular BiologyLaboratory) and DDBJ (DNA Databank of Japan). BLAST or similar tools canbe used to search identified sequences against a sequence database.Search hits can then be used to sort the identified sequences intoappropriate portions (described hereafter), for example.

In some embodiments, a read may uniquely or non-uniquely map to portionsin a reference genome. A read is considered as “uniquely mapped” if italigns with a single sequence in the reference genome. A read isconsidered as “non-uniquely mapped” if it aligns with two or moresequences in the reference genome. In some embodiments, non-uniquelymapped reads are eliminated from further analysis (e.g. quantification).A certain, small degree of mismatch (0-1) may be allowed to account forsingle nucleotide polymorphisms that may exist between the referencegenome and the reads from individual samples being mapped, in certainembodiments. In some embodiments, no degree of mismatch is allowed for aread mapped to a reference sequence.

As used herein, the term “reference genome” can refer to any particularknown, sequenced or characterized genome, whether partial or complete,of any organism or virus which may be used to reference identifiedsequences from a subject. For example, a reference genome used for humansubjects as well as many other organisms can be found at the NationalCenter for Biotechnology Information at World Wide Web URLncbi.nlm.nih.gov. A “genome” refers to the complete genetic informationof an organism or virus, expressed in nucleic acid sequences. As usedherein, a reference sequence or reference genome often is an assembledor partially assembled genomic sequence from an individual or multipleindividuals. In some embodiments, a reference genome is an assembled orpartially assembled genomic sequence from one or more human individuals.In some embodiments, a reference genome comprises sequences assigned tochromosomes.

In certain embodiments, mappability is assessed for a genomic region(e.g., portion, genomic portion). Mappability is the ability tounambiguously align a nucleotide sequence read to a portion of areference genome, typically up to a specified number of mismatches,including, for example, 0, 1, 2 or more mismatches. For a given genomicregion, the expected mappability can be estimated using a sliding-windowapproach of a preset read length and averaging the resulting read-levelmappability values. Genomic regions comprising stretches of uniquenucleotide sequence sometimes have a high mappability value.

For paired-end sequencing, reads may be mapped to a reference genome byuse of a suitable mapping and/or alignment program, non-limitingexamples of which include BWA (Li H. and Durbin R. (2009) Bioinformatics25, 1754-60), Novoalign [Novocraft (2010)], Bowtie (Langmead B, et al.,(2009) Genome Biol. 10:R25), SOAP2 (Li R, et al., (2009) Bioinformatics25, 1966-67), BFAST (Homer N, et al., (2009) PLoS ONE 4, e7767), GASSST(Rizk, G. and Lavenier, D. (2010) Bioinformatics 26, 2534-2540), andMPscan (Rivals E., et al. (2009) Lecture Notes in Computer Science 5724,246-260), and the like. Paired-end reads may be mapped and/or alignedusing a suitable short read alignment program. Non-limiting examples ofshort read alignment programs include BarraCUDA, BFAST, BLASTN, BLAT,Bowtie, BWA, CASHX, CUDA-EC, CUSHAW, CUSHAW2, drFAST, ELAND, ERNE,GNUMAP, GEM, GensearchNGS, GMAP, Geneious Assembler, iSAAC, LAST, MAQ,mrFAST, mrsFAST, MOSAIK, MPscan, Novoalign, NovoalignCS, Novocraft,NextGENe, Omixon, PALMapper, Partek, PASS, PerM, QPalma, RazerS, REAL,cREAL, RMAP, rNA, RTG, Segemehl, SeqMap, Shrec, SHRiMP, SLIDER, SOAP,SOAP2, SOAP3, SOCS, SSAHA, SSAHA2, Stampy, SToRM, Subread, Subjunc,Taipan, UGENE, VelociMapper, TimeLogic, XpressAlign, ZOOM, the like orcombinations thereof. Paired-end reads are often mapped to opposing endsof the same polynucleotide fragment, according to a reference genome. Insome embodiments, read mates are mapped independently. In someembodiments, information from both sequence reads (i.e., from each end)is factored in the mapping process. A reference genome is often used todetermine and/or infer the sequence of nucleic acids located betweenpaired-end read mates. The term “discordant read pairs” as used hereinrefers to a paired-end read comprising a pair of read mates, where oneor both read mates fail to unambiguously map to the same region of areference genome defined, in part, by a segment of contiguousnucleotides. In some embodiments discordant read pairs are paired-endread mates that map to unexpected locations of a reference genome.Non-limiting examples of unexpected locations of a reference genomeinclude (i) two different chromosomes, (ii) locations separated by morethan a predetermined fragment size (e.g., more than 300 bp, more than500 bp, more than 1000 bp, more than 5000 bp, or more than 10,000 bp),(iii) an orientation inconsistent with a reference sequence (e.g.,opposite orientations), the like or a combination thereof. In someembodiments discordant read mates are identified according to a length(e.g., an average length, a predetermined fragment size) or expectedlength of template polynucleotide fragments in a sample. For example,read mates that map to a location that is separated by more than theaverage length or expected length of polynucleotide fragments in asample are sometimes identified as discordant read pairs. Read pairsthat map in opposite orientation are sometimes determined by taking thereverse complement of one of the reads and comparing the alignment ofboth reads using the same strand of a reference sequence. Discordantread pairs can be identified by any suitable method and/or algorithmknown in the art or described herein (e.g., SVDetect, Lumpy,BreakDancer, BreakDancerMax, CREST, DELLY, the like or combinationsthereof).

Portions

In some embodiments, mapped sequence reads are grouped togetheraccording to various parameters and assigned to particular genomicportions (e.g., portions of a reference genome). A “portion” also may bereferred to herein as a “genomic section,” “bin,” “partition,” “portionof a reference genome,” “portion of a chromosome” or “genomic portion.”

A portion often is defined by partitioning of a genome according to oneor more features. Non-limiting examples of certain partitioning featuresinclude length (e.g., fixed length, non-fixed length) and otherstructural features. Genomic portions sometimes include one or more ofthe following features: fixed length, non-fixed length, random length,non-random length, equal length, unequal length (e.g., at least two ofthe genomic portions are of unequal length), do not overlap (e.g., the3′ ends of the genomic portions sometimes abut the 5′ ends of adjacentgenomic portions), overlap (e.g., at least two of the genomic portionsoverlap), contiguous, consecutive, not contiguous, and not consecutive.Genomic portions sometimes are about 1 to about 1,000 kilobases inlength (e.g., about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40,45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400, 500,600, 700, 800, 900 kilobases in length), about 5 to about 500 kilobasesin length, about 10 to about 100 kilobases in length, or about 40 toabout 60 kilobases in length.

Partitioning sometimes is based on, or is based in part on, certaininformational features, such as, information content and informationgain, for example. Non-limiting examples of certain informationalfeatures include speed and/or convenience of alignment, sequencingcoverage variability, GC content (e.g., stratified GC content,particular GC contents, high or low GC content), uniformity of GCcontent, other measures of sequence content (e.g., fraction ofindividual nucleotides, fraction of pyrimidines or purines, fraction ofnatural vs. non-natural nucleic acids, fraction of methylatednucleotides, and CpG content), methylation state, duplex meltingtemperature, amenability to sequencing or PCR, uncertainty valueassigned to individual portions of a reference genome, and/or a targetedsearch for particular features. In some embodiments, information contentmay be quantified using a p-value profile measuring the significance ofparticular genomic locations for distinguishing between groups ofconfirmed normal and abnormal subjects (e.g. euploid and trisomysubjects, respectively).

In some embodiments, partitioning a genome may eliminate similar regions(e.g., identical or homologous regions or sequences) across a genome andonly keep unique regions. Regions removed during partitioning may bewithin a single chromosome, may be one or more chromosomes, or may spanmultiple chromosomes. In some embodiments, a partitioned genome isreduced and optimized for faster alignment, often focusing on uniquelyidentifiable sequences.

In some embodiments, genomic portions result from a partitioning basedon non-overlapping fixed size, which results in consecutive,non-overlapping portions of fixed length. Such portions often areshorter than a chromosome and often are shorter than a copy numbervariation (or copy number alteration) region (e.g., a region that isduplicated or is deleted), the latter of which can be referred to as asegment. A “segment” or “genomic segment” often includes two or morefixed-length genomic portions, and often includes two or moreconsecutive fixed-length portions (e.g., about 2 to about 100 suchportions (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90 such portions)).

Multiple portions sometimes are analyzed in groups, and sometimes readsmapped to portions are quantified according to a particular group ofgenomic portions. Where portions are partitioned by structural featuresand correspond to regions in a genome, portions sometimes are groupedinto one or more segments and/or one or more regions. Non-limitingexamples of regions include sub-chromosome (i.e., shorter than achromosome), chromosome, autosome, sex chromosome and combinationsthereof. One or more sub-chromosome regions sometimes are genes, genefragments, regulatory sequences, introns, exons, segments (e.g., asegment spanning a copy number alteration region), microduplications,microdeletions and the like. A region sometimes is smaller than achromosome of interest or is the same size of a chromosome of interest,and sometimes is smaller than a reference chromosome or is the same sizeas a reference chromosome.

Filtering and/or Selecting Portions

In some embodiments, one or more processing steps can comprise one ormore portion filtering steps and/or portion selection steps. The term“filtering” as used herein refers to removing portions or portions of areference genome from consideration. In certain embodiments one or moreportions are filtered (e.g., subjected to a filtering process) therebyproviding filtered portions. In some embodiments a filtering processremoves certain portions and retains portions (e.g., a subset ofportions). Following a filtering process, retained portions are oftenreferred to herein as filtered portions.

Portions of a reference genome can be selected for removal based on anysuitable criteria, including but not limited to redundant data (e.g.,redundant or overlapping mapped reads), non-informative data (e.g.,portions of a reference genome with zero median counts), portions of areference genome with over represented or under represented sequences,noisy data, the like, or combinations of the foregoing. A filteringprocess often involves removing one or more portions of a referencegenome from consideration and subtracting the counts in the one or moreportions of a reference genome selected for removal from the counted orsummed counts for the portions of a reference genome, chromosome orchromosomes, or genome under consideration. In some embodiments,portions of a reference genome can be removed successively (e.g., one ata time to allow evaluation of the effect of removal of each individualportion), and in certain embodiments all portions of a reference genomemarked for removal can be removed at the same time. In some embodiments,portions of a reference genome characterized by a variance above orbelow a certain level are removed, which sometimes is referred to hereinas filtering “noisy” portions of a reference genome. In certainembodiments, a filtering process comprises obtaining data points from adata set that deviate from the mean profile level of a portion, achromosome, or part of a chromosome by a predetermined multiple of theprofile variance, and in certain embodiments, a filtering processcomprises removing data points from a data set that do not deviate fromthe mean profile level of a portion, a chromosome or part of achromosome by a predetermined multiple of the profile variance. In someembodiments, a filtering process is utilized to reduce the number ofcandidate portions of a reference genome analyzed for the presence orabsence of a genetic variation/genetic alteration and/or copy numberalteration (e.g., aneuploidy, microdeletion, microduplication). Reducingthe number of candidate portions of a reference genome analyzed for thepresence or absence of a genetic variation/genetic alteration and/orcopy number alteration often reduces the complexity and/ordimensionality of a data set, and sometimes increases the speed ofsearching for and/or identifying genetic variations/genetic alterationand/or copy number alterations by two or more orders of magnitude.

Portions may be processed (e.g., filtered and/or selected) by anysuitable method and according to any suitable parameter. Non-limitingexamples of features and/or parameters that can be used to filter and/orselect portions include redundant data (e.g., redundant or overlappingmapped reads), non-informative data (e.g., portions of a referencegenome with zero mapped counts), portions of a reference genome withover represented or under represented sequences, noisy data, counts,count variability, coverage, mappability, variability, a repeatabilitymeasure, read density, variability of read density, a level ofuncertainty, guanine-cytosine (GC) content, CCF fragment length and/orread length (e.g., a fragment length ratio (FLR), a fetal ratiostatistic (FRS)), DNasel-sensitivity, methylation state, acetylation,histone distribution, chromatin structure, percent repeats, the like orcombinations thereof. Portions can be filtered and/or selected accordingto any suitable feature or parameter that correlates with a feature orparameter listed or described herein. Portions can be filtered and/orselected according to features or parameters that are specific to aportion (e.g., as determined for a single portion according to multiplesamples) and/or features or parameters that are specific to a sample(e.g., as determined for multiple portions within a sample). In someembodiments portions are filtered and/or removed according to relativelylow mappability, relatively high variability, a high level ofuncertainty, relatively long CCF fragment lengths (e.g., low FRS, lowFLR), relatively large fraction of repetitive sequences, high GCcontent, low GC content, low counts, zero counts, high counts, the like,or combinations thereof. In some embodiments portions (e.g., a subset ofportions) are selected according to suitable level of mappability,variability, level of uncertainty, fraction of repetitive sequences,count, GC content, the like, or combinations thereof In some embodimentsportions (e.g., a subset of portions) are selected according torelatively short CCF fragment lengths (e.g., high FRS, high FLR). Countsand/or reads mapped to portions are sometimes processed (e.g.,normalized) prior to and/or after filtering or selecting portions (e.g.,a subset of portions). In some embodiments counts and/or reads mapped toportions are not processed prior to and/or after filtering or selectingportions (e.g., a subset of portions).

In some embodiments, portions may be filtered according to a measure oferror (e.g., standard deviation, standard error, calculated variance,p-value, mean absolute error (MAE), average absolute deviation and/ormean absolute deviation (MAD)). In certain instances, a measure of errormay refer to count variability. In some embodiments portions arefiltered according to count variability. In certain embodiments countvariability is a measure of error determined for counts mapped to aportion (i.e., portion) of a reference genome for multiple samples(e.g., multiple sample obtained from multiple subjects, e.g., 50 ormore, 100 or more, 500 or more 1000 or more, 5000 or more or 10,000 ormore subjects). In some embodiments, portions with a count variabilityabove a pre-determined upper range are filtered (e.g., excluded fromconsideration). In some embodiments portions with a count variabilitybelow a pre-determined lower range are filtered (e.g., excluded fromconsideration). In some embodiments, portions with a count variabilityoutside a pre-determined range are filtered (e.g., excluded fromconsideration). In some embodiments portions with a count variabilitywithin a pre-determined range are selected (e.g., used for determiningthe presence or absence of a copy number alteration). In someembodiments, count variability of portions represents a distribution(e.g., a normal distribution). In some embodiments portions are selectedwithin a quantile of the distribution. In some embodiments portionswithin a 99% quantile of the distribution of count variability areselected.

Sequence reads from any suitable number of samples can be utilized toidentify a subset of portions that meet one or more criteria, parametersand/or features described herein. Sequence reads from a group of samplesfrom multiple subjects sometimes are utilized. In some embodiments, themultiple subjects include pregnant females. In some embodiments, themultiple subjects include healthy subjects. In some embodiments, themultiple subjects include cancer patients. One or more samples from eachof the multiple subjects can be addressed (e.g., 1 to about 20 samplesfrom each subject (e.g., about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18 or 19 samples)), and a suitable number of subjectsmay be addressed (e.g., about 2 to about 10,000 subjects (e.g., about10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400,500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000,9000 subjects)). In some embodiments, sequence reads from the same testsample(s) from the same subject are mapped to portions in the referencegenome and are used to generate the subset of portions.

Portions can be selected and/or filtered by any suitable method. In someembodiments portions are selected according to visual inspection ofdata, graphs, plots and/or charts. In certain embodiments portions areselected and/or filtered (e.g., in part) by a system or a machinecomprising one or more microprocessors and memory. In some embodimentsportions are selected and/or filtered (e.g., in part) by anon-transitory computer-readable storage medium with an executableprogram stored thereon, where the program instructs a microprocessor toperform the selecting and/or filtering.

In some embodiments, sequence reads derived from a sample are mapped toall or most portions of a reference genome and a pre-selected subset ofportions are thereafter selected. For example, a subset of portions towhich reads from fragments under a particular length thresholdpreferentially map may be selected. Certain methods for pre-selecting asubset of portions are described in U.S. Patent Application PublicationNo. 2014/0180594, which is incorporated by reference herein. Reads froma selected subset of portions often are utilized in further steps of adetermination of the presence or absence of a genetic variation orgenetic alteration, for example. Often, reads from portions not selectedare not utilized in further steps of a determination of the presence orabsence of a genetic variation or genetic alteration (e.g., reads in thenon-selected portions are removed or filtered).

In some embodiments portions associated with read densities (e.g., wherea read density is for a portion) are removed by a filtering process andread densities associated with removed portions are not included in adetermination of the presence or absence of a copy number alteration(e.g., a chromosome aneuploidy, microduplication, microdeletion). Insome embodiments a read density profile comprises and/or comprises orconsists of read densities of filtered portions. Portions are sometimesfiltered according to a distribution of counts and/or a distribution ofread densities. In some embodiments portions are filtered according to adistribution of counts and/or read densities where the counts and/orread densities are obtained from one or more reference samples. One ormore reference samples may be referred to herein as a training set. Insome embodiments portions are filtered according to a distribution ofcounts and/or read densities where the counts and/or read densities areobtained from one or more test samples. In some embodiments portions arefiltered according to a measure of uncertainty for a read densitydistribution. In certain embodiments, portions that demonstrate a largedeviation in read densities are removed by a filtering process. Forexample, a distribution of read densities (e.g., a distribution ofaverage mean, or median read densities) can be determined, where eachread density in the distribution maps to the same portion. A measure ofuncertainty (e.g., a MAD) can be determined by comparing a distributionof read densities for multiple samples where each portion of a genome isassociated with measure of uncertainty. According to the foregoingexample, portions can be filtered according to a measure of uncertainty(e.g., a standard deviation (SD), a MAD) associated with each portionand a predetermined threshold. In certain instances, portions comprisingMAD values within the acceptable range are retained and portionscomprising MAD values outside of the acceptable range are removed fromconsideration by a filtering process. In some embodiments, according tothe foregoing example, portions comprising read densities values (e.g.,median, average or mean read densities) outside a pre-determined measureof uncertainty are often removed from consideration by a filteringprocess. In some embodiments portions comprising read densities values(e.g., median, average or mean read densities) outside an inter-quartilerange of a distribution are removed from consideration by a filteringprocess. In some embodiments portions comprising read densities valuesoutside more than 2 times, 3 times, 4 times or 5 times an inter-quartilerange of a distribution are removed from consideration by a filteringprocess. In some embodiments portions comprising read densities valuesoutside more than 2 sigma, 3 sigma, 4 sigma, 5 sigma, 6 sigma, 7 sigmaor 8 sigma (e.g., where sigma is a range defined by a standarddeviation) are removed from consideration by a filtering process.

Sequence Read Quantification

Sequence reads that are mapped or partitioned based on a selectedfeature or variable can be quantified to determine the amount or numberof reads that are mapped to one or more portions (e.g., portion of areference genome), in some embodiments. In certain embodiments thequantity of sequence reads that are mapped to a portion or segment isreferred to as a count or read density.

A count often is associated with a genomic portion. In some embodimentsa count is determined from some or all of the sequence reads mapped to(i.e., associated with) a portion. In certain embodiments, a count isdetermined from some or all of the sequence reads mapped to a group ofportions (e.g., portions in a segment or region (described herein)).

A count can be determined by a suitable method, operation ormathematical process. A count sometimes is the direct sum of allsequence reads mapped to a genomic portion or a group of genomicportions corresponding to a segment, a group of portions correspondingto a sub-region of a genome (e.g., copy number variation region, copynumber alteration region, copy number duplication region, copy numberdeletion region, microduplication region, microdeletion region,chromosome region, autosome region, sex chromosome region) and/orsometimes is a group of portions corresponding to a genome. A readquantification sometimes is a ratio, and sometimes is a ratio of aquantification for portion(s) in region a to a quantification forportion(s) in region b. Region a sometimes is one portion, segmentregion, copy number variation region, copy number alteration region,copy number duplication region, copy number deletion region,microduplication region, microdeletion region, chromosome region,autosome region and/or sex chromosome region. Region b independentlysometimes is one portion, segment region, copy number variation region,copy number alteration region, copy number duplication region, copynumber deletion region, microduplication region, microdeletion region,chromosome region, autosome region, sex chromosome region, a regionincluding all autosomes, a region including sex chromosomes and/or aregion including all chromosomes.

In some embodiments, a count is derived from raw sequence reads and/orfiltered sequence reads. In certain embodiments a count is an average,mean or sum of sequence reads mapped to a genomic portion or group ofgenomic portions (e.g., genomic portions in a region). In someembodiments, a count is associated with an uncertainty value. A countsometimes is adjusted. A count may be adjusted according to sequencereads associated with a genomic portion or group of portions that havebeen weighted, removed, filtered, normalized, adjusted, averaged,derived as a mean, derived as a median, added, or combination thereof.

A sequence read quantification sometimes is a read density. A readdensity may be determined and/or generated for one or more segments of agenome. In certain instances, a read density may be determined and/orgenerated for one or more chromosomes. In some embodiments a readdensity comprises a quantitative measure of counts of sequence readsmapped to a segment or portion of a reference genome. A read density canbe determined by a suitable process. In some embodiments a read densityis determined by a suitable distribution and/or a suitable distributionfunction. Non-limiting examples of a distribution function include aprobability function, probability distribution function, probabilitydensity function (PDF), a kernel density function (kernel densityestimation), a cumulative distribution function, probability massfunction, discrete probability distribution, an absolutely continuousunivariate distribution, the like, any suitable distribution, orcombinations thereof. A read density may be a density estimation derivedfrom a suitable probability density function. A density estimation isthe construction of an estimate, based on observed data, of anunderlying probability density function. In some embodiments a readdensity comprises a density estimation (e.g., a probability densityestimation, a kernel density estimation). A read density may begenerated according to a process comprising generating a densityestimation for each of the one or more portions of a genome where eachportion comprises counts of sequence reads. A read density may begenerated for normalized and/or weighted counts mapped to a portion orsegment. In some instances, each read mapped to a portion or segment maycontribute to a read density, a value (e.g., a count) equal to itsweight obtained from a normalization process described herein. In someembodiments read densities for one or more portions or segments areadjusted. Read densities can be adjusted by a suitable method. Forexample, read densities for one or more portions can be weighted and/ornormalized.

Reads quantified for a given portion or segment can be from one sourceor different sources. In one example, reads may be obtained from nucleicacid from a subject having cancer or suspected of having cancer. In suchcircumstances, reads mapped to one or more portions often are readsrepresentative of both healthy cells (i.e., non-cancer cells) and cancercells (e.g., tumor cells). In certain embodiments, some of the readsmapped to a portion are from cancer cell nucleic acid and some of thereads mapped to the same portion are from non-cancer cell nucleic acid.In another example, reads may be obtained from a nucleic acid samplefrom a pregnant female bearing a fetus. In such circumstances, readsmapped to one or more portions often are reads representative of boththe fetus and the mother of the fetus (e.g., a pregnant female subject).In certain embodiments some of the reads mapped to a portion are from afetal genome and some of the reads mapped to the same portion are from amaternal genome.

Levels

In some embodiments, a value (e.g., a number, a quantitative value) isascribed to a level. A level can be determined by a suitable method,operation or mathematical process (e.g., a processed level). A leveloften is, or is derived from, counts (e.g., normalized counts) for a setof portions. In some embodiments a level of a portion is substantiallyequal to the total number of counts mapped to a portion (e.g., counts,normalized counts). Often a level is determined from counts that areprocessed, transformed or manipulated by a suitable method, operation ormathematical process known in the art. In some embodiments a level isderived from counts that are processed and non-limiting examples ofprocessed counts include weighted, removed, filtered, normalized,adjusted, averaged, derived as a mean (e.g., mean level), added,subtracted, transformed counts or combination thereof. In someembodiments a level comprises counts that are normalized (e.g.,normalized counts of portions). A level can be for counts normalized bya suitable process, non-limiting examples of which are described herein.A level can comprise normalized counts or relative amounts of counts. Insome embodiments a level is for counts or normalized counts of two ormore portions that are averaged and the level is referred to as anaverage level. In some embodiments a level is for a set of portionshaving a mean count or mean of normalized counts which is referred to asa mean level. In some embodiments a level is derived for portions thatcomprise raw and/or filtered counts. In some embodiments, a level isbased on counts that are raw. In some embodiments a level is associatedwith an uncertainty value (e.g., a standard deviation, a MAD). In someembodiments a level is represented by a Z-score or p-value.

A level for one or more portions is synonymous with a “genomic sectionlevel” herein. The term “level” as used herein is sometimes synonymouswith the term “elevation.” A determination of the meaning of the term“level” can be determined from the context in which it is used. Forexample, the term “level,” when used in the context of portions,profiles, reads and/or counts often means an elevation. The term“level,” when used in the context of a substance or composition (e.g.,level of RNA, plexing level) often refers to an amount. The term“level,” when used in the context of uncertainty (e.g., level of error,level of confidence, level of deviation, level of uncertainty) oftenrefers to an amount.

Normalized or non-normalized counts for two or more levels (e.g., two ormore levels in a profile) can sometimes be mathematically manipulated(e.g., added, multiplied, averaged, normalized, the like or combinationthereof) according to levels. For example, normalized or non-normalizedcounts for two or more levels can be normalized according to one, someor all of the levels in a profile. In some embodiments normalized ornon-normalized counts of all levels in a profile are normalizedaccording to one level in the profile. In some embodiments normalized ornon-normalized counts of a first level in a profile are normalizedaccording to normalized or non-normalized counts of a second level inthe profile.

Non-limiting examples of a level (e.g., a first level, a second level)are a level for a set of portions comprising processed counts, a levelfor a set of portions comprising a mean, median or average of counts, alevel for a set of portions comprising normalized counts, the like orany combination thereof In some embodiments, a first level and a secondlevel in a profile are derived from counts of portions mapped to thesame chromosome. In some embodiments, a first level and a second levelin a profile are derived from counts of portions mapped to differentchromosomes.

In some embodiments a level is determined from normalized ornon-normalized counts mapped to one or more portions. In someembodiments, a level is determined from normalized or non-normalizedcounts mapped to two or more portions, where the normalized counts foreach portion often are about the same. There can be variation in counts(e.g., normalized counts) in a set of portions for a level. In a set ofportions for a level there can be one or more portions having countsthat are significantly different than in other portions of the set(e.g., peaks and/or dips). Any suitable number of normalized ornon-normalized counts associated with any suitable number of portionscan define a level.

In some embodiments one or more levels can be determined from normalizedor non-normalized counts of all or some of the portions of a genome.Often a level can be determined from all or some of the normalized ornon-normalized counts of a chromosome, or part thereof In someembodiments, two or more counts derived from two or more portions (e.g.,a set of portions) determine a level. In some embodiments two or morecounts (e.g., counts from two or more portions) determine a level. Insome embodiments, counts from 2 to about 100,000 portions determine alevel. In some embodiments, counts from 2 to about 50,000, 2 to about40,000, 2 to about 30,000, 2 to about 20,000, 2 to about 10,000, 2 toabout 5000, 2 to about 2500, 2 to about 1250, 2 to about 1000, 2 toabout 500, 2 to about 250, 2 to about 100 or 2 to about 60 portionsdetermine a level. In some embodiments counts from about 10 to about 50portions determine a level. In some embodiments counts from about 20 toabout 40 or more portions determine a level. In some embodiments, alevel comprises counts from about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60 or more portions.In some embodiments, a level corresponds to a set of portions (e.g., aset of portions of a reference genome, a set of portions of a chromosomeor a set of portions of a part of a chromosome).

In some embodiments, a level is determined for normalized ornon-normalized counts of portions that are contiguous. In someembodiments portions (e.g., a set of portions) that are contiguousrepresent neighboring regions of a genome or neighboring regions of achromosome or gene. For example, two or more contiguous portions, whenaligned by merging the portions end to end, can represent a sequenceassembly of a DNA sequence longer than each portion. For example two ormore contiguous portions can represent of an intact genome, chromosome,gene, intron, exon or part thereof In some embodiments a level isdetermined from a collection (e.g., a set) of contiguous portions and/ornon-contiguous portions.

Data Processing and Normalization

Mapped sequence reads that have been counted are referred to herein asraw data, since the data represents unmanipulated counts (e.g., rawcounts). In some embodiments, sequence read data in a data set can beprocessed further (e.g., mathematically and/or statisticallymanipulated) and/or displayed to facilitate providing an outcome. Incertain embodiments, data sets, including larger data sets, may benefitfrom pre-processing to facilitate further analysis. Pre-processing ofdata sets sometimes involves removal of redundant and/or uninformativeportions or portions of a reference genome (e.g., portions of areference genome with uninformative data, redundant mapped reads,portions with zero median counts, over represented or under representedsequences). Without being limited by theory, data processing and/orpreprocessing may (i) remove noisy data, (ii) remove uninformative data,(iii) remove redundant data, (iv) reduce the complexity of larger datasets, and/or (v) facilitate transformation of the data from one forminto one or more other forms. The terms “pre-processing” and“processing” when utilized with respect to data or data sets arecollectively referred to herein as “processing.” Processing can renderdata more amenable to further analysis, and can generate an outcome insome embodiments. In some embodiments one or more or all processingmethods (e.g., normalization methods, portion filtering, mapping,validation, the like or combinations thereof) are performed by aprocessor, a micro-processor, a computer, in conjunction with memoryand/or by a microprocessor controlled apparatus.

The term “noisy data” as used herein refers to (a) data that has asignificant variance between data points when analyzed or plotted, (b)data that has a significant standard deviation (e.g., greater than 3standard deviations), (c) data that has a significant standard error ofthe mean, the like, and combinations of the foregoing. Noisy datasometimes occurs due to the quantity and/or quality of starting material(e.g., nucleic acid sample), and sometimes occurs as part of processesfor preparing or replicating DNA used to generate sequence reads. Incertain embodiments, noise results from certain sequences beingoverrepresented when prepared using PCR-based methods. Methods describedherein can reduce or eliminate the contribution of noisy data, andtherefore reduce the effect of noisy data on the provided outcome.

The terms “uninformative data,” “uninformative portions of a referencegenome,” and “uninformative portions” as used herein refer to portions,or data derived therefrom, having a numerical value that issignificantly different from a predetermined threshold value or fallsoutside a predetermined cutoff range of values. The terms “threshold”and “threshold value” herein refer to any number that is calculatedusing a qualifying data set and serves as a limit of diagnosis of agenetic variation or genetic alteration (e.g., a copy number alteration,an aneuploidy, a microduplication, a microdeletion, a chromosomalaberration, and the like). In certain embodiments, a threshold isexceeded by results obtained by methods described herein and a subjectis diagnosed with a copy number alteration. A threshold value or rangeof values often is calculated by mathematically and/or statisticallymanipulating sequence read data (e.g., from a reference and/or subject),in some embodiments, and in certain embodiments, sequence read datamanipulated to generate a threshold value or range of values is sequenceread data (e.g., from a reference and/or subject). In some embodiments,an uncertainty value is determined. An uncertainty value generally is ameasure of variance or error and can be any suitable measure of varianceor error. In some embodiments an uncertainty value is a standarddeviation, standard error, calculated variance, p-value, or meanabsolute deviation (MAD). In some embodiments an uncertainty value canbe calculated according to a formula described herein.

Any suitable procedure can be utilized for processing data setsdescribed herein. Non-limiting examples of procedures suitable for usefor processing data sets include filtering, normalizing, weighting,monitoring peak heights, monitoring peak areas, monitoring peak edges,peak level analysis, peak width analysis, peak edge location analysis,peak lateral tolerances, determining area ratios, mathematicalprocessing of data, statistical processing of data, application ofstatistical algorithms, analysis with fixed variables, analysis withoptimized variables, plotting data to identify patterns or trends foradditional processing, the like and combinations of the foregoing. Insome embodiments, data sets are processed based on various features(e.g., GC content, redundant mapped reads, centromere regions, telomereregions, the like and combinations thereof) and/or variables (e.g.,subject gender, subject age, subject ploidy, percent contribution ofcancer cell nucleic acid, fetal gender, maternal age, maternal ploidy,percent contribution of fetal nucleic acid, the like or combinationsthereof). In certain embodiments, processing data sets as describedherein can reduce the complexity and/or dimensionality of large and/orcomplex data sets. A non-limiting example of a complex data set includessequence read data generated from one or more test subjects and aplurality of reference subjects of different ages and ethnicbackgrounds. In some embodiments, data sets can include from thousandsto millions of sequence reads for each test and/or reference subject.

Data processing can be performed in any number of steps, in certainembodiments. For example, data may be processed using only a singleprocessing procedure in some embodiments, and in certain embodimentsdata may be processed using 1 or more, 5 or more, 10 or more or 20 ormore processing steps (e.g., 1 or more processing steps, 2 or moreprocessing steps, 3 or more processing steps, 4 or more processingsteps, 5 or more processing steps, 6 or more processing steps, 7 or moreprocessing steps, 8 or more processing steps, 9 or more processingsteps, 10 or more processing steps, 11 or more processing steps, 12 ormore processing steps, 13 or more processing steps, 14 or moreprocessing steps, 15 or more processing steps, 16 or more processingsteps, 17 or more processing steps, 18 or more processing steps, 19 ormore processing steps, or 20 or more processing steps). In someembodiments, processing steps may be the same step repeated two or moretimes (e.g., filtering two or more times, normalizing two or moretimes), and in certain embodiments, processing steps may be two or moredifferent processing steps (e.g., filtering, normalizing; normalizing,monitoring peak heights and edges; filtering, normalizing, normalizingto a reference, statistical manipulation to determine p-values, and thelike), carried out simultaneously or sequentially. In some embodiments,any suitable number and/or combination of the same or differentprocessing steps can be utilized to process sequence read data tofacilitate providing an outcome. In certain embodiments, processing datasets by the criteria described herein may reduce the complexity and/ordimensionality of a data set.

In some embodiments one or more processing steps can comprise one ormore normalization steps. Normalization can be performed by a suitablemethod described herein or known in the art. In certain embodiments,normalization comprises adjusting values measured on different scales toa notionally common scale. In certain embodiments, normalizationcomprises a sophisticated mathematical adjustment to bring probabilitydistributions of adjusted values into alignment. In some embodimentsnormalization comprises aligning distributions to a normal distribution.In certain embodiments normalization comprises mathematical adjustmentsthat allow comparison of corresponding normalized values for differentdatasets in a way that eliminates the effects of certain grossinfluences (e.g., error and anomalies). In certain embodimentsnormalization comprises scaling. Normalization sometimes comprisesdivision of one or more data sets by a predetermined variable orformula. Normalization sometimes comprises subtraction of one or moredata sets by a predetermined variable or formula. Non-limiting examplesof normalization methods include portion-wise normalization,normalization by GC content, median count (median bin count, medianportion count) normalization, linear and nonlinear least squaresregression, LOESS, GC LOESS, LOWESS (locally weighted scatterplotsmoothing), principal component normalization, repeat masking (RM),GC-normalization and repeat masking (GCRM), cQn and/or combinationsthereof. In some embodiments, the determination of a presence or absenceof a copy number alteration (e.g., an aneuploidy, a microduplication, amicrodeletion) utilizes a normalization method (e.g., portion-wisenormalization, normalization by GC content, median count (median bincount, median portion count) normalization, linear and nonlinear leastsquares regression, LOESS, GC LOESS, LOWESS (locally weightedscatterplot smoothing), principal component normalization, repeatmasking (RM), GC-normalization and repeat masking (GCRM), cQn, anormalization method known in the art and/or a combination thereof).Described in greater detail hereafter are certain examples ofnormalization processes that can be utilized, such as LOESSnormalization, principal component normalization, and hybridnormalization methods, for example. Aspects of certain normalizationprocesses also are described, for example, in International PatentApplication Publication No. WO2013/052913 and International PatentApplication Publication No. WO2015/051163, each of which is incorporatedby reference herein.

Any suitable number of normalizations can be used. In some embodiments,data sets can be normalized 1 or more, 5 or more, 10 or more or even 20or more times. Data sets can be normalized to values (e.g., normalizingvalue) representative of any suitable feature or variable (e.g., sampledata, reference data, or both). Non-limiting examples of types of datanormalizations that can be used include normalizing raw count data forone or more selected test or reference portions to the total number ofcounts mapped to the chromosome or the entire genome on which theselected portion or sections are mapped; normalizing raw count data forone or more selected portions to a median reference count for one ormore portions or the chromosome on which a selected portion is mapped;normalizing raw count data to previously normalized data or derivativesthereof; and normalizing previously normalized data to one or more otherpredetermined normalization variables. Normalizing a data set sometimeshas the effect of isolating statistical error, depending on the featureor property selected as the predetermined normalization variable.Normalizing a data set sometimes also allows comparison of datacharacteristics of data having different scales, by bringing the data toa common scale (e.g., predetermined normalization variable). In someembodiments, one or more normalizations to a statistically derived valuecan be utilized to minimize data differences and diminish the importanceof outlying data. Normalizing portions, or portions of a referencegenome, with respect to a normalizing value sometimes is referred to as“portion-wise normalization.”

In certain embodiments, a processing step can comprise one or moremathematical and/or statistical manipulations. Any suitable mathematicaland/or statistical manipulation, alone or in combination, may be used toanalyze and/or manipulate a data set described herein. Any suitablenumber of mathematical and/or statistical manipulations can be used. Insome embodiments, a data set can be mathematically and/or statisticallymanipulated 1 or more, 5 or more, 10 or more or 20 or more times.Non-limiting examples of mathematical and statistical manipulations thatcan be used include addition, subtraction, multiplication, division,algebraic functions, least squares estimators, curve fitting,differential equations, rational polynomials, double polynomials,orthogonal polynomials, z-scores, p-values, chi values, phi values,analysis of peak levels, determination of peak edge locations,calculation of peak area ratios, analysis of median chromosomal level,calculation of mean absolute deviation, sum of squared residuals, mean,standard deviation, standard error, the like or combinations thereof. Amathematical and/or statistical manipulation can be performed on all ora portion of sequence read data, or processed products thereof.Non-limiting examples of data set variables or features that can bestatistically manipulated include raw counts, filtered counts,normalized counts, peak heights, peak widths, peak areas, peak edges,lateral tolerances, P-values, median levels, mean levels, countdistribution within a genomic region, relative representation of nucleicacid species, the like or combinations thereof.

In some embodiments, a processing step can comprise the use of one ormore statistical algorithms. Any suitable statistical algorithm, aloneor in combination, may be used to analyze and/or manipulate a data setdescribed herein. Any suitable number of statistical algorithms can beused. In some embodiments, a data set can be analyzed using 1 or more, 5or more, 10 or more or 20 or more statistical algorithms. Non-limitingexamples of statistical algorithms suitable for use with methodsdescribed herein include principal component analysis, decision trees,counternulls, multiple comparisons, omnibus test, Behrens-Fisherproblem, bootstrapping, Fisher's method for combining independent testsof significance, null hypothesis, type I error, type II error, exacttest, one-sample Z test, two-sample Z test, one-sample t-test, pairedt-test, two-sample pooled t-test having equal variances, two-sampleunpooled t-test having unequal variances, one-proportion z-test,two-proportion z-test pooled, two-proportion z-test unpooled, one-samplechi-square test, two-sample F test for equality of variances, confidenceinterval, credible interval, significance, meta analysis, simple linearregression, robust linear regression, the like or combinations of theforegoing. Non-limiting examples of data set variables or features thatcan be analyzed using statistical algorithms include raw counts,filtered counts, normalized counts, peak heights, peak widths, peakedges, lateral tolerances, P-values, median levels, mean levels, countdistribution within a genomic region, relative representation of nucleicacid species, the like or combinations thereof.

In certain embodiments, a data set can be analyzed by utilizing multiple(e.g., 2 or more) statistical algorithms (e.g., least squaresregression, principal component analysis, linear discriminant analysis,quadratic discriminant analysis, bagging, neural networks, supportvector machine models, random forests, classification tree models,K-nearest neighbors, logistic regression and/or smoothing) and/ormathematical and/or statistical manipulations (e.g., referred to hereinas manipulations). The use of multiple manipulations can generate anN-dimensional space that can be used to provide an outcome, in someembodiments. In certain embodiments, analysis of a data set by utilizingmultiple manipulations can reduce the complexity and/or dimensionalityof the data set. For example, the use of multiple manipulations on areference data set can generate an N-dimensional space (e.g.,probability plot) that can be used to represent the presence or absenceof a genetic variation/genetic alteration and/or copy number alteration,depending on the status of the reference samples (e.g., positive ornegative for a selected copy number alteration). Analysis of testsamples using a substantially similar set of manipulations can be usedto generate an N-dimensional point for each of the test samples. Thecomplexity and/or dimensionality of a test subject data set sometimes isreduced to a single value or N-dimensional point that can be readilycompared to the N-dimensional space generated from the reference data.Test sample data that fall within the N-dimensional space populated bythe reference subject data are indicative of a genetic statussubstantially similar to that of the reference subjects. Test sampledata that fall outside of the N-dimensional space populated by thereference subject data are indicative of a genetic status substantiallydissimilar to that of the reference subjects. In some embodiments,references are euploid or do not otherwise have a geneticvariation/genetic alteration and/or copy number alteration and/ormedical condition.

After data sets have been counted, optionally filtered, normalized, andoptionally weighted the processed data sets can be further manipulatedby one or more filtering and/or normalizing and/or weighting procedures,in some embodiments. A data set that has been further manipulated by oneor more filtering and/or normalizing and/or weighting procedures can beused to generate a profile, in certain embodiments. The one or morefiltering and/or normalizing and/or weighting procedures sometimes canreduce data set complexity and/or dimensionality, in some embodiments.An outcome can be provided based on a data set of reduced complexityand/or dimensionality. In some embodiments, a profile plot of processeddata further manipulated by weighting, for example, is generated tofacilitate classification and/or providing an outcome. An outcome can beprovided based on a profile plot of weighted data, for example.

Filtering or weighting of portions can be performed at one or moresuitable points in an analysis. For example, portions may be filtered orweighted before or after sequence reads are mapped to portions of areference genome. Portions may be filtered or weighted before or afteran experimental bias for individual genome portions is determined insome embodiments. In certain embodiments, portions may be filtered orweighted before or after levels are calculated.

After data sets have been counted, optionally filtered, normalized, andoptionally weighted, the processed data sets can be manipulated by oneor more mathematical and/or statistical (e.g., statistical functions orstatistical algorithm) manipulations, in some embodiments. In certainembodiments, processed data sets can be further manipulated bycalculating Z-scores for one or more selected portions, chromosomes, orportions of chromosomes. In some embodiments, processed data sets can befurther manipulated by calculating P-values. In certain embodiments,mathematical and/or statistical manipulations include one or moreassumptions pertaining to ploidy and/or fraction of a minority species(e.g., fraction of cancer cell nucleic acid; fetal fraction). In someembodiments, a profile plot of processed data further manipulated by oneor more statistical and/or mathematical manipulations is generated tofacilitate classification and/or providing an outcome. An outcome can beprovided based on a profile plot of statistically and/or mathematicallymanipulated data. An outcome provided based on a profile plot ofstatistically and/or mathematically manipulated data often includes oneor more assumptions pertaining to ploidy and/or fraction of a minorityspecies (e.g., fraction of cancer cell nucleic acid; fetal fraction).

In some embodiments, analysis and processing of data can include the useof one or more assumptions. A suitable number or type of assumptions canbe utilized to analyze or process a data set. Non-limiting examples ofassumptions that can be used for data processing and/or analysis includesubject ploidy, cancer cell contribution, maternal ploidy, fetalcontribution, prevalence of certain sequences in a reference population,ethnic background, prevalence of a selected medical condition in relatedfamily members, parallelism between raw count profiles from differentpatients and/or runs after GC-normalization and repeat masking (e.g.,GCRM), identical matches represent PCR artifacts (e.g., identical baseposition), assumptions inherent in a nucleic acid quantification assay(e.g., fetal quantifier assay (FQA)), assumptions regarding twins (e.g.,if 2 twins and only 1 is affected the effective fetal fraction is only50% of the total measured fetal fraction (similarly for triplets,quadruplets and the like)), cell free DNA (e.g., cfDNA) uniformly coversthe entire genome, the like and combinations thereof.

In those instances where the quality and/or depth of mapped sequencereads does not permit an outcome prediction of the presence or absenceof a genetic variation/genetic alteration and/or copy number alterationat a desired confidence level (e.g., 95% or higher confidence level),based on the normalized count profiles, one or more additionalmathematical manipulation algorithms and/or statistical predictionalgorithms, can be utilized to generate additional numerical valuesuseful for data analysis and/or providing an outcome. The term“normalized count profile” as used herein refers to a profile generatedusing normalized counts. Examples of methods that can be used togenerate normalized counts and normalized count profiles are describedherein. As noted, mapped sequence reads that have been counted can benormalized with respect to test sample counts or reference samplecounts. In some embodiments, a normalized count profile can be presentedas a plot.

Described in greater detail hereafter are non-limiting examples ofprocessing steps and normalization methods that can be utilized, such asnormalizing to a window (static or sliding), weighting, determining biasrelationship, LOESS normalization, principal component normalization,hybrid normalization, generating a profile and performing a comparison.

Normalizing to a Window (Static or Sliding)

In certain embodiments, a processing step comprises normalizing to astatic window, and in some embodiments, a processing step comprisesnormalizing to a moving or sliding window. The term “window” as usedherein refers to one or more portions chosen for analysis, and sometimesis used as a reference for comparison (e.g., used for normalizationand/or other mathematical or statistical manipulation). The term“normalizing to a static window” as used herein refers to anormalization process using one or more portions selected for comparisonbetween a test subject and reference subject data set. In someembodiments the selected portions are utilized to generate a profile. Astatic window generally includes a predetermined set of portions that donot change during manipulations and/or analysis. The terms “normalizingto a moving window” and “normalizing to a sliding window” as used hereinrefer to normalizations performed to portions localized to the genomicregion (e.g., immediate surrounding portions, adjacent portion orsections, and the like) of a selected test portion, where one or moreselected test portions are normalized to portions immediatelysurrounding the selected test portion. In certain embodiments, theselected portions are utilized to generate a profile. A sliding ormoving window normalization often includes repeatedly moving or slidingto an adjacent test portion, and normalizing the newly selected testportion to portions immediately surrounding or adjacent to the newlyselected test portion, where adjacent windows have one or more portionsin common. In certain embodiments, a plurality of selected test portionsand/or chromosomes can be analyzed by a sliding window process.

In some embodiments, normalizing to a sliding or moving window cangenerate one or more values, where each value represents normalizationto a different set of reference portions selected from different regionsof a genome (e.g., chromosome). In certain embodiments, the one or morevalues generated are cumulative sums (e.g., a numerical estimate of theintegral of the normalized count profile over the selected portion,domain (e.g., part of chromosome), or chromosome). The values generatedby the sliding or moving window process can be used to generate aprofile and facilitate arriving at an outcome. In some embodiments,cumulative sums of one or more portions can be displayed as a functionof genomic position. Moving or sliding window analysis sometimes is usedto analyze a genome for the presence or absence of microdeletions and/ormicroduplications. In certain embodiments, displaying cumulative sums ofone or more portions is used to identify the presence or absence ofregions of copy number alteration (e.g., microdeletion,microduplication).

Weighting

In some embodiments, a processing step comprises a weighting. The terms“weighted,” “weighting” or “weight function” or grammatical derivativesor equivalents thereof, as used herein, refer to a mathematicalmanipulation of a portion or all of a data set sometimes utilized toalter the influence of certain data set features or variables withrespect to other data set features or variables (e.g., increase ordecrease the significance and/or contribution of data contained in oneor more portions or portions of a reference genome, based on the qualityor usefulness of the data in the selected portion or portions of areference genome). A weighting function can be used to increase theinfluence of data with a relatively small measurement variance, and/orto decrease the influence of data with a relatively large measurementvariance, in some embodiments. For example, portions of a referencegenome with underrepresented or low quality sequence data can be “downweighted” to minimize the influence on a data set, whereas selectedportions of a reference genome can be “up weighted” to increase theinfluence on a data set. A non-limiting example of a weighting functionis [1/(standard deviation)²]. Weighting portions sometimes removesportion dependencies. In some embodiments one or more portions areweighted by an eigen function (e.g., an eigenfunction). In someembodiments an eigen function comprises replacing portions withorthogonal eigen-portions. A weighting step sometimes is performed in amanner substantially similar to a normalizing step. In some embodiments,a data set is adjusted (e.g., divided, multiplied, added, subtracted) bya predetermined variable (e.g., weighting variable). In someembodiments, a data set is divided by a predetermined variable (e.g.,weighting variable). A predetermined variable (e.g., minimized targetfunction, Phi) often is selected to weigh different parts of a data setdifferently (e.g., increase the influence of certain data types whiledecreasing the influence of other data types).

Bias Relationships

In some embodiments, a processing step comprises determining a biasrelationship. For example, one or more relationships may be generatedbetween local genome bias estimates and bias frequencies. The term“relationship” as use herein refers to a mathematical and/or a graphicalrelationship between two or more variables or values. A relationship canbe generated by a suitable mathematical and/or graphical process.Non-limiting examples of a relationship include a mathematical and/orgraphical representation of a function, a correlation, a distribution, alinear or non-linear equation, a line, a regression, a fittedregression, the like or a combination thereof. Sometimes a relationshipcomprises a fitted relationship. In some embodiments a fittedrelationship comprises a fitted regression. Sometimes a relationshipcomprises two or more variables or values that are weighted. In someembodiments a relationship comprise a fitted regression where one ormore variables or values of the relationship a weighted. Sometimes aregression is fitted in a weighted fashion. Sometimes a regression isfitted without weighting. In certain embodiments, generating arelationship comprises plotting or graphing.

In certain embodiments, a relationship is generated between GC densitiesand GC density frequencies. In some embodiments generating arelationship between (i) GC densities and (ii) GC density frequenciesfor a sample provides a sample GC density relationship. In someembodiments generating a relationship between (i) GC densities and (ii)GC density frequencies for a reference provides a reference GC densityrelationship. In some embodiments, where local genome bias estimates areGC densities, a sample bias relationship is a sample GC densityrelationship and a reference bias relationship is a reference GC densityrelationship. GC densities of a reference GC density relationship and/ora sample GC density relationship are often representations (e.g.,mathematical or quantitative representation) of local GC content.

In some embodiments a relationship between local genome bias estimatesand bias frequencies comprises a distribution. In some embodiments arelationship between local genome bias estimates and bias frequenciescomprises a fitted relationship (e.g., a fitted regression). In someembodiments a relationship between local genome bias estimates and biasfrequencies comprises a fitted linear or non-linear regression (e.g., apolynomial regression). In certain embodiments a relationship betweenlocal genome bias estimates and bias frequencies comprises a weightedrelationship where local genome bias estimates and/or bias frequenciesare weighted by a suitable process. In some embodiments a weightedfitted relationship (e.g., a weighted fitting) can be obtained by aprocess comprising a quantile regression, parameterized distributions oran empirical distribution with interpolation. In certain embodiments arelationship between local genome bias estimates and bias frequenciesfor a test sample, a reference or part thereof, comprises a polynomialregression where local genome bias estimates are weighted. In someembodiments a weighed fitted model comprises weighting values of adistribution. Values of a distribution can be weighted by a suitableprocess. In some embodiments, values located near tails of adistribution are provided less weight than values closer to the medianof the distribution. For example, for a distribution between localgenome bias estimates (e.g., GC densities) and bias frequencies (e.g.,GC density frequencies), a weight is determined according to the biasfrequency for a given local genome bias estimate, where local genomebias estimates comprising bias frequencies closer to the mean of adistribution are provided greater weight than local genome biasestimates comprising bias frequencies further from the mean.

In some embodiments, a processing step comprises normalizing sequenceread counts by comparing local genome bias estimates of sequence readsof a test sample to local genome bias estimates of a reference (e.g., areference genome, or part thereof). In some embodiments, counts ofsequence reads are normalized by comparing bias frequencies of localgenome bias estimates of a test sample to bias frequencies of localgenome bias estimates of a reference. In some embodiments counts ofsequence reads are normalized by comparing a sample bias relationshipand a reference bias relationship, thereby generating a comparison.

Counts of sequence reads may be normalized according to a comparison oftwo or more relationships. In certain embodiments two or morerelationships are compared thereby providing a comparison that is usedfor reducing local bias in sequence reads (e.g., normalizing counts).Two or more relationships can be compared by a suitable method. In someembodiments a comparison comprises adding, subtracting, multiplyingand/or dividing a first relationship from a second relationship. Incertain embodiments comparing two or more relationships comprises a useof a suitable linear regression and/or a non-linear regression. Incertain embodiments comparing two or more relationships comprises asuitable polynomial regression (e.g., a 3^(rd) order polynomialregression). In some embodiments a comparison comprises adding,subtracting, multiplying and/or dividing a first regression from asecond regression. In some embodiments two or more relationships arecompared by a process comprising an inferential framework of multipleregressions. In some embodiments two or more relationships are comparedby a process comprising a suitable multivariate analysis. In someembodiments two or more relationships are compared by a processcomprising a basis function (e.g., a blending function, e.g., polynomialbases, Fourier bases, or the like), splines, a radial basis functionand/or wavelets.

In certain embodiments a distribution of local genome bias estimatescomprising bias frequencies for a test sample and a reference iscompared by a process comprising a polynomial regression where localgenome bias estimates are weighted. In some embodiments a polynomialregression is generated between (i) ratios, each of which ratioscomprises bias frequencies of local genome bias estimates of a referenceand bias frequencies of local genome bias estimates of a sample and (ii)local genome bias estimates. In some embodiments a polynomial regressionis generated between (i) a ratio of bias frequencies of local genomebias estimates of a reference to bias frequencies of local genome biasestimates of a sample and (ii) local genome bias estimates. In someembodiments a comparison of a distribution of local genome biasestimates for reads of a test sample and a reference comprisesdetermining a log ratio (e.g., a log 2 ratio) of bias frequencies oflocal genome bias estimates for the reference and the sample. In someembodiments a comparison of a distribution of local genome biasestimates comprises dividing a log ratio (e.g., a log 2 ratio) of biasfrequencies of local genome bias estimates for the reference by a logratio (e.g., a log 2 ratio) of bias frequencies of local genome biasestimates for the sample.

Normalizing counts according to a comparison typically adjusts somecounts and not others. Normalizing counts sometimes adjusts all countsand sometimes does not adjust any counts of sequence reads. A count fora sequence read sometimes is normalized by a process that comprisesdetermining a weighting factor and sometimes the process does notinclude directly generating and utilizing a weighting factor.Normalizing counts according to a comparison sometimes comprisesdetermining a weighting factor for each count of a sequence read. Aweighting factor is often specific to a sequence read and is applied toa count of a specific sequence read. A weighting factor is oftendetermined according to a comparison of two or more bias relationships(e.g., a sample bias relationship compared to a reference biasrelationship). A normalized count is often determined by adjusting acount value according to a weighting factor.

Adjusting a count according to a weighting factor sometimes includesadding, subtracting, multiplying and/or dividing a count for a sequenceread by a weighting factor. A weighting factor and/or a normalized countsometimes are determined from a regression (e.g., a regression line). Anormalized count is sometimes obtained directly from a regression line(e.g., a fitted regression line) resulting from a comparison betweenbias frequencies of local genome bias estimates of a reference (e.g., areference genome) and a test sample. In some embodiments each count of aread of a sample is provided a normalized count value according to acomparison of (i) bias frequencies of a local genome bias estimates ofreads compared to (ii) bias frequencies of a local genome bias estimatesof a reference. In certain embodiments, counts of sequence readsobtained for a sample are normalized and bias in the sequence reads isreduced.

LOESS Normalization

In some embodiments, a processing step comprises a LOESS normalization.LOESS is a regression modeling method known in the art that combinesmultiple regression models in a k-nearest-neighbor-based meta-model.LOESS is sometimes referred to as a locally weighted polynomialregression. GC LOESS, in some embodiments, applies an LOESS model to therelationship between template count (e.g., sequence reads, counts) andGC composition for portions of a reference genome. Plotting a smoothcurve through a set of data points using LOESS is sometimes called anLOESS curve, particularly when each smoothed value is given by aweighted quadratic least squares regression over the span of values ofthe y-axis scattergram criterion variable. For each point in a data set,the LOESS method fits a low-degree polynomial to a subset of the data,with explanatory variable values near the point whose response is beingestimated. The polynomial is fitted using weighted least squares, givingmore weight to points near the point whose response is being estimatedand less weight to points further away. The value of the regressionfunction for a point is then obtained by evaluating the local polynomialusing the explanatory variable values for that data point. The LOESS fitis sometimes considered complete after regression function values havebeen computed for each of the data points. Many of the details of thismethod, such as the degree of the polynomial model and the weights, areflexible.

Principal Component Analysis

In some embodiments, a processing step comprises a principal componentanalysis (PCA). In some embodiments, sequence read counts (e.g.,sequence read counts of a test sample) is adjusted according to aprincipal component analysis (PCA). In some embodiments a read densityprofile (e.g., a read density profile of a test sample) is adjustedaccording to a principal component analysis (PCA). A read densityprofile of one or more reference samples and/or a read density profileof a test subject can be adjusted according to a PCA. Removing bias froma read density profile by a PCA related process is sometimes referred toherein as adjusting a profile. A PCA can be performed by a suitable PCAmethod, or a variation thereof. Non-limiting examples of a PCA methodinclude a canonical correlation analysis (CCA), a Karhunen-Loèvetransform (KLT), a Hotelling transform, a proper orthogonaldecomposition (POD), a singular value decomposition (SVD) of X, aneigenvalue decomposition (EVD) of XTX, a factor analysis, anEckart-Young theorem, a Schmidt-Mirsky theorem, empirical orthogonalfunctions (EDF), an empirical eigenfunction decomposition, an empiricalcomponent analysis, quasiharmonic modes, a spectral decomposition, anempirical modal analysis, the like, variations or combinations thereof.A PCA often identifies and/or adjusts for one or more biases in a readdensity profile. A bias identified and/or adjusted for by a PCA issometimes referred to herein as a principal component. In someembodiments one or more biases can be removed by adjusting a readdensity profile according to one or more principal component using asuitable method. A read density profile can be adjusted by adding,subtracting, multiplying and/or dividing one or more principalcomponents from a read density profile. In some embodiments, one or morebiases can be removed from a read density profile by subtracting one ormore principal components from a read density profile. Although bias ina read density profile is often identified and/or quantitated by a PCAof a profile, principal components are often subtracted from a profileat the level of read densities. A PCA often identifies one or moreprincipal components. In some embodiments a PCA identifies a 1^(st),2^(nd), 3^(rd), 4^(th), 5^(th), 6^(th), 7^(th), 8^(th), 9^(th) and a10^(th) or more principal components. In certain embodiments, 1, 2, 3,4, 5, 6, 7, 8, 9, 10 or more principal components are used to adjust aprofile. In certain embodiments, 5 principal components are used toadjust a profile. Often, principal components are used to adjust aprofile in the order of appearance in a PCA. For example, where threeprincipal components are subtracted from a read density profile, a1^(st), 2^(nd) and 3^(rd) principal component are used. Sometimes a biasidentified by a principal component comprises a feature of a profilethat is not used to adjust a profile. For example, a PCA may identify acopy number alteration (e.g., an aneuploidy, microduplication,microdeletion, deletion, translocation, insertion) and/or a genderdifference as a principal component. Thus, in some embodiments, one ormore principal components are not used to adjust a profile. For example,sometimes a 1^(st), 2^(nd) and 4^(th) principal component are used toadjust a profile where a 3^(rd) principal component is not used toadjust a profile.

A principal component can be obtained from a PCA using any suitablesample or reference. In some embodiments principal components areobtained from a test sample (e.g., a test subject). In some embodimentsprincipal components are obtained from one or more references (e.g.,reference samples, reference sequences, a reference set). In certaininstances, a PCA is performed on a median read density profile obtainedfrom a training set comprising multiple samples resulting in theidentification of a 1^(st) principal component and a 2^(nd) principalcomponent. In some embodiments, principal components are obtained from aset of subjects devoid of a copy number alteration in question. In someembodiments, principal components are obtained from a set of knowneuploids. Principal component are often identified according to a PCAperformed using one or more read density profiles of a reference (e.g.,a training set). One or more principal components obtained from areference are often subtracted from a read density profile of a testsubject thereby providing an adjusted profile.

Hybrid Normalization

In some embodiments, a processing step comprises a hybrid normalizationmethod. A hybrid normalization method may reduce bias (e.g., GC bias),in certain instances. A hybrid normalization, in some embodiments,comprises (i) an analysis of a relationship of two variables (e.g.,counts and GC content) and (ii) selection and application of anormalization method according to the analysis. A hybrid normalization,in certain embodiments, comprises (i) a regression (e.g., a regressionanalysis) and (ii) selection and application of a normalization methodaccording to the regression. In some embodiments counts obtained for afirst sample (e.g., a first set of samples) are normalized by adifferent method than counts obtained from another sample (e.g., asecond set of samples). In some embodiments counts obtained for a firstsample (e.g., a first set of samples) are normalized by a firstnormalization method and counts obtained from a second sample (e.g., asecond set of samples) are normalized by a second normalization method.For example, in certain embodiments a first normalization methodcomprises use of a linear regression and a second normalization methodcomprises use of a non-linear regression (e.g., a LOESS, GC-LOESS,LOWESS regression, LOESS smoothing).

In some embodiments a hybrid normalization method is used to normalizesequence reads mapped to portions of a genome or chromosome (e.g.,counts, mapped counts, mapped reads). In certain embodiments raw countsare normalized and in some embodiments adjusted, weighted, filtered orpreviously normalized counts are normalized by a hybrid normalizationmethod. In certain embodiments, levels or Z-scores are normalized. Insome embodiments counts mapped to selected portions of a genome orchromosome are normalized by a hybrid normalization approach. Counts canrefer to a suitable measure of sequence reads mapped to portions of agenome, non-limiting examples of which include raw counts (e.g.,unprocessed counts), normalized counts (e.g., normalized by LOESS,principal component, or a suitable method), portion levels (e.g.,average levels, mean levels, median levels, or the like), Z-scores, thelike, or combinations thereof. The counts can be raw counts or processedcounts from one or more samples (e.g., a test sample, a sample from apregnant female). In some embodiments counts are obtained from one ormore samples obtained from one or more subjects.

In some embodiments a normalization method (e.g., the type ofnormalization method) is selected according to a regression (e.g., aregression analysis) and/or a correlation coefficient. A regressionanalysis refers to a statistical technique for estimating a relationshipamong variables (e.g., counts and GC content). In some embodiments aregression is generated according to counts and a measure of GC contentfor each portion of multiple portions of a reference genome. A suitablemeasure of GC content can be used, non-limiting examples of whichinclude a measure of guanine, cytosine, adenine, thymine, purine (GC),or pyrimidine (AT or ATU) content, melting temperature (T_(m)) (e.g.,denaturation temperature, annealing temperature, hybridizationtemperature), a measure of free energy, the like or combinationsthereof. A measure of guanine (G), cytosine (C), adenine (A), thymine(T), purine (GC), or pyrimidine (AT or ATU) content can be expressed asa ratio or a percentage. In some embodiments any suitable ratio orpercentage is used, non-limiting examples of which include GC/AT,GC/total nucleotide, GC/A, GC/T, AT/total nucleotide, AT/GC, AT/G, AT/C,G/A, C/A, G/T, G/A, G/AT, C/T, the like or combinations thereof. In someembodiments a measure of GC content is a ratio or percentage of GC tototal nucleotide content. In some embodiments a measure of GC content isa ratio or percentage of GC to total nucleotide content for sequencereads mapped to a portion of reference genome. In certain embodimentsthe GC content is determined according to and/or from sequence readsmapped to each portion of a reference genome and the sequence reads areobtained from a sample. In some embodiments a measure of GC content isnot determined according to and/or from sequence reads. In certainembodiments, a measure of GC content is determined for one or moresamples obtained from one or more subjects.

In some embodiments generating a regression comprises generating aregression analysis or a correlation analysis. A suitable regression canbe used, non-limiting examples of which include a regression analysis,(e.g., a linear regression analysis), a goodness of fit analysis, aPearson's correlation analysis, a rank correlation, a fraction ofvariance unexplained, Nash-Sutcliffe model efficiency analysis,regression model validation, proportional reduction in loss, root meansquare deviation, the like or a combination thereof. In some embodimentsa regression line is generated. In certain embodiments generating aregression comprises generating a linear regression. In certainembodiments generating a regression comprises generating a non-linearregression (e.g., an LOESS regression, an LOWESS regression).

In some embodiments a regression determines the presence or absence of acorrelation (e.g., a linear correlation), for example between counts anda measure of GC content. In some embodiments a regression (e.g., alinear regression) is generated and a correlation coefficient isdetermined. In some embodiments a suitable correlation coefficient isdetermined, non-limiting examples of which include a coefficient ofdetermination, an R² value, a Pearson's correlation coefficient, or thelike.

In some embodiments goodness of fit is determined for a regression(e.g., a regression analysis, a linear regression). Goodness of fitsometimes is determined by visual or mathematical analysis. Anassessment sometimes includes determining whether the goodness of fit isgreater for a non-linear regression or for a linear regression. In someembodiments a correlation coefficient is a measure of a goodness of fit.In some embodiments an assessment of a goodness of fit for a regressionis determined according to a correlation coefficient and/or acorrelation coefficient cutoff value. In some embodiments an assessmentof a goodness of fit comprises comparing a correlation coefficient to acorrelation coefficient cutoff value. In some embodiments an assessmentof a goodness of fit for a regression is indicative of a linearregression. For example, in certain embodiments, a goodness of fit isgreater for a linear regression than for a non-linear regression and theassessment of the goodness of fit is indicative of a linear regression.In some embodiments an assessment is indicative of a linear regressionand a linear regression is used to normalized the counts. In someembodiments an assessment of a goodness of fit for a regression isindicative of a non-linear regression. For example, in certainembodiments, a goodness of fit is greater for a non-linear regressionthan for a linear regression and the assessment of the goodness of fitis indicative of a non-linear regression. In some embodiments anassessment is indicative of a non-linear regression and a non-linearregression is used to normalized the counts.

In some embodiments an assessment of a goodness of fit is indicative ofa linear regression when a correlation coefficient is equal to orgreater than a correlation coefficient cutoff. In some embodiments anassessment of a goodness of fit is indicative of a non-linear regressionwhen a correlation coefficient is less than a correlation coefficientcutoff. In some embodiments a correlation coefficient cutoff ispre-determined. In some embodiments a correlation coefficient cut-off isabout 0.5 or greater, about 0.55 or greater, about 0.6 or greater, about0.65 or greater, about 0.7 or greater, about 0.75 or greater, about 0.8or greater or about 0.85 or greater.

In some embodiments a specific type of regression is selected (e.g., alinear or non-linear regression) and, after the regression is generated,counts are normalized by subtracting the regression from the counts. Insome embodiments subtracting a regression from the counts providesnormalized counts with reduced bias (e.g., GC bias). In some embodimentsa linear regression is subtracted from the counts. In some embodiments anon-linear regression (e.g., a LOESS, GC-LOESS, LOWESS regression) issubtracted from the counts. Any suitable method can be used to subtracta regression line from the counts. For example, if counts x are derivedfrom portion i (e.g., a portion i) comprising a GC content of 0.5 and aregression line determines counts y at a GC content of 0.5, thenx-y=normalized counts for portion i. In some embodiments counts arenormalized prior to and/or after subtracting a regression. In someembodiments, counts normalized by a hybrid normalization approach areused to generate levels, Z-scores, levels and/or profiles of a genome ora part thereof. In certain embodiments, counts normalized by a hybridnormalization approach are analyzed by methods described herein todetermine the presence or absence of a genetic variation or geneticalteration (e.g., copy number alteration).

In some embodiments a hybrid normalization method comprises filtering orweighting one or more portions before or after normalization. A suitablemethod of filtering portions, including methods of filtering portions(e.g., portions of a reference genome) described herein can be used. Insome embodiments, portions (e.g., portions of a reference genome) arefiltered prior to applying a hybrid normalization method. In someembodiments, only counts of sequencing reads mapped to selected portions(e.g., portions selected according to count variability) are normalizedby a hybrid normalization. In some embodiments counts of sequencingreads mapped to filtered portions of a reference genome (e.g., portionsfiltered according to count variability) are removed prior to utilizinga hybrid normalization method. In some embodiments a hybridnormalization method comprises selecting or filtering portions (e.g.,portions of a reference genome) according to a suitable method (e.g., amethod described herein). In some embodiments a hybrid normalizationmethod comprises selecting or filtering portions (e.g., portions of areference genome) according to an uncertainty value for counts mapped toeach of the portions for multiple test samples. In some embodiments ahybrid normalization method comprises selecting or filtering portions(e.g., portions of a reference genome) according to count variability.In some embodiments a hybrid normalization method comprises selecting orfiltering portions (e.g., portions of a reference genome) according toGC content, repetitive elements, repetitive sequences, introns, exons,the like or a combination thereof.

Profiles

In some embodiments, a processing step comprises generating one or moreprofiles (e.g., profile plot) from various aspects of a data set orderivation thereof (e.g., product of one or more mathematical and/orstatistical data processing steps known in the art and/or describedherein).

The term “profile” as used herein refers to a product of a mathematicaland/or statistical manipulation of data that can facilitateidentification of patterns and/or correlations in large quantities ofdata. A “profile” often includes values resulting from one or moremanipulations of data or data sets, based on one or more criteria. Aprofile often includes multiple data points. Any suitable number of datapoints may be included in a profile depending on the nature and/orcomplexity of a data set. In certain embodiments, profiles may include 2or more data points, 3 or more data points, 5 or more data points, 10 ormore data points, 24 or more data points, 25 or more data points, 50 ormore data points, 100 or more data points, 500 or more data points, 1000or more data points, 5000 or more data points, 10,000 or more datapoints, or 100,000 or more data points.

In some embodiments, a profile is representative of the entirety of adata set, and in certain embodiments, a profile is representative of apart or subset of a data set. That is, a profile sometimes includes oris generated from data points representative of data that has not beenfiltered to remove any data, and sometimes a profile includes or isgenerated from data points representative of data that has been filteredto remove unwanted data. In some embodiments, a data point in a profilerepresents the results of data manipulation for a portion. In certainembodiments, a data point in a profile includes results of datamanipulation for groups of portions. In some embodiments, groups ofportions may be adjacent to one another, and in certain embodiments,groups of portions may be from different parts of a chromosome orgenome.

Data points in a profile derived from a data set can be representativeof any suitable data categorization. Non-limiting examples of categoriesinto which data can be grouped to generate profile data points include:portions based on size, portions based on sequence features (e.g., GCcontent, AT content, position on a chromosome (e.g., short arm, longarm, centromere, telomere), and the like), levels of expression,chromosome, the like or combinations thereof. In some embodiments, aprofile may be generated from data points obtained from another profile(e.g., normalized data profile renormalized to a different normalizingvalue to generate a renormalized data profile). In certain embodiments,a profile generated from data points obtained from another profilereduces the number of data points and/or complexity of the data set.Reducing the number of data points and/or complexity of a data set oftenfacilitates interpretation of data and/or facilitates providing anoutcome.

A profile (e.g., a genomic profile, a chromosome profile, a profile of apart of a chromosome) often is a collection of normalized ornon-normalized counts for two or more portions. A profile often includesat least one level, and often comprises two or more levels (e.g., aprofile often has multiple levels). A level generally is for a set ofportions having about the same counts or normalized counts. Levels aredescribed in greater detail herein. In certain embodiments, a profilecomprises one or more portions, which portions can be weighted, removed,filtered, normalized, adjusted, averaged, derived as a mean, added,subtracted, processed or transformed by any combination thereof. Aprofile often comprises normalized counts mapped to portions definingtwo or more levels, where the counts are further normalized according toone of the levels by a suitable method. Often counts of a profile (e.g.,a profile level) are associated with an uncertainty value.

A profile comprising one or more levels is sometimes padded (e.g., holepadding). Padding (e.g., hole padding) refers to a process ofidentifying and adjusting levels in a profile that are due to copynumber alterations (e.g., microduplications or microdeletions in apatient's genome, maternal microduplications or microdeletions). In someembodiments, levels are padded that are due to microduplications ormicrodeletions in a tumor or a fetus. Microduplications ormicrodeletions in a profile can, in some embodiments, artificially raiseor lower the overall level of a profile (e.g., a profile of achromosome) leading to false positive or false negative determinationsof a chromosome aneuploidy (e.g., a trisomy). In some embodiments,levels in a profile that are due to microduplications and/or deletionsare identified and adjusted (e.g., padded and/or removed) by a processsometimes referred to as padding or hole padding.

A profile comprising one or more levels can include a first level and asecond level. In some embodiments a first level is different (e.g.,significantly different) than a second level. In some embodiments afirst level comprises a first set of portions, a second level comprisesa second set of portions and the first set of portions is not a subsetof the second set of portions. In certain embodiments, a first set ofportions is different than a second set of portions from which a firstand second level are determined. In some embodiments a profile can havemultiple first levels that are different (e.g., significantly different,e.g., have a significantly different value) than a second level withinthe profile. In some embodiments a profile comprises one or more firstlevels that are significantly different than a second level within theprofile and one or more of the first levels are adjusted. In someembodiments a first level within a profile is removed from the profileor adjusted (e.g., padded). A profile can comprise multiple levels thatinclude one or more first levels significantly different than one ormore second levels and often the majority of levels in a profile aresecond levels, which second levels are about equal to one another. Insome embodiments greater than 50%, greater than 60%, greater than 70%,greater than 80%, greater than 90% or greater than 95% of the levels ina profile are second levels.

A profile sometimes is displayed as a plot. For example, one or morelevels representing counts (e.g., normalized counts) of portions can beplotted and visualized. Non-limiting examples of profile plots that canbe generated include raw count (e.g., raw count profile or raw profile),normalized count, portion-weighted, z-score, p-value, area ratio versusfitted ploidy, median level versus ratio between fitted and measuredminority species fraction, principal components, the like, orcombinations thereof. Profile plots allow visualization of themanipulated data, in some embodiments. In certain embodiments, a profileplot can be utilized to provide an outcome (e.g., area ratio versusfitted ploidy, median level versus ratio between fitted and measuredminority species fraction, principal components). The terms “raw countprofile plot” or “raw profile plot” as used herein refer to a plot ofcounts in each portion in a region normalized to total counts in aregion (e.g., genome, portion, chromosome, chromosome portions of areference genome or a part of a chromosome). In some embodiments, aprofile can be generated using a static window process, and in certainembodiments, a profile can be generated using a sliding window process.

A profile generated for a test subject sometimes is compared to aprofile generated for one or more reference subjects, to facilitateinterpretation of mathematical and/or statistical manipulations of adata set and/or to provide an outcome. In some embodiments, a profile isgenerated based on one or more starting assumptions, e.g., assumptionsdescribed herein. In certain embodiments, a test profile often centersaround a predetermined value representative of the absence of a copynumber alteration, and often deviates from a predetermined value inareas corresponding to the genomic location in which the copy numberalteration is located in the test subject, if the test subject possessedthe copy number alteration. In test subjects at risk for, or sufferingfrom a medical condition associated with a copy number alteration, thenumerical value for a selected portion is expected to vary significantlyfrom the predetermined value for non-affected genomic locations.Depending on starting assumptions (e.g., fixed ploidy or optimizedploidy, fixed fraction of cancer cell nucleic acid or optimized fractionof cancer cell nucleic acid, fixed fetal fraction or optimized fetalfraction, or combinations thereof) the predetermined threshold or cutoffvalue or threshold range of values indicative of the presence or absenceof a copy number alteration can vary while still providing an outcomeuseful for determining the presence or absence of a copy numberalteration. In some embodiments, a profile is indicative of and/orrepresentative of a phenotype.

In some embodiments, the use of one or more reference samples that aresubstantially free of a copy number alteration in question can be usedto generate a reference count profile (e.g., a reference median countprofile), which may result in a predetermined value representative ofthe absence of the copy number alteration, and often deviates from apredetermined value in areas corresponding to the genomic location inwhich the copy number alteration is located in the test subject, if thetest subject possessed the copy number alteration. In test subjects atrisk for, or suffering from a medical condition associated with a copynumber alteration, the numerical value for the selected portion orsections is expected to vary significantly from the predetermined valuefor non-affected genomic locations. In certain embodiments, the use ofone or more reference samples known to carry the copy number alterationin question can be used to generate a reference count profile (areference median count profile), which may result in a predeterminedvalue representative of the presence of the copy number alteration, andoften deviates from a predetermined value in areas corresponding to thegenomic location in which a test subject does not carry the copy numberalteration. In test subjects not at risk for, or suffering from amedical condition associated with a copy number alteration, thenumerical value for the selected portion or sections is expected to varysignificantly from the predetermined value for affected genomiclocations.

By way of a non-limiting example, normalized sample and/or referencecount profiles can be obtained from raw sequence read data by (a)calculating reference median counts for selected chromosomes, portionsor parts thereof from a set of references known not to carry a copynumber alteration, (b) removal of uninformative portions from thereference sample raw counts (e.g., filtering); (c) normalizing thereference counts for all remaining portions of a reference genome to thetotal residual number of counts (e.g., sum of remaining counts afterremoval of uninformative portions of a reference genome) for thereference sample selected chromosome or selected genomic location,thereby generating a normalized reference subject profile; (d) removingthe corresponding portions from the test subject sample; and (e)normalizing the remaining test subject counts for one or more selectedgenomic locations to the sum of the residual reference median counts forthe chromosome or chromosomes containing the selected genomic locations,thereby generating a normalized test subject profile. In certainembodiments, an additional normalizing step with respect to the entiregenome, reduced by the filtered portions in (b), can be included between(c) and (d).

In some embodiments a read density profile is determined. In someembodiments a read density profile comprises at least one read density,and often comprises two or more read densities (e.g., a read densityprofile often comprises multiple read densities). In some embodiments, aread density profile comprises a suitable quantitative value (e.g., amean, a median, a Z-score, or the like). A read density profile oftencomprises values resulting from one or more read densities. A readdensity profile sometimes comprises values resulting from one or moremanipulations of read densities based on one or more adjustments (e.g.,normalizations). In some embodiments a read density profile comprisesunmanipulated read densities. In some embodiments, one or more readdensity profiles are generated from various aspects of a data setcomprising read densities, or a derivation thereof (e.g., product of oneor more mathematical and/or statistical data processing steps known inthe art and/or described herein). In certain embodiments, a read densityprofile comprises normalized read densities. In some embodiments a readdensity profile comprises adjusted read densities. In certainembodiments a read density profile comprises raw read densities (e.g.,unmanipulated, not adjusted or normalized), normalized read densities,weighted read densities, read densities of filtered portions, z-scoresof read densities, p-values of read densities, integral values of readdensities (e.g., area under the curve), average, mean or median readdensities, principal components, the like, or combinations thereof.Often read densities of a read density profile and/or a read densityprofile is associated with a measure of uncertainty (e.g., a MAD). Incertain embodiments, a read density profile comprises a distribution ofmedian read densities. In some embodiments a read density profilecomprises a relationship (e.g., a fitted relationship, a regression, orthe like) of a plurality of read densities. For example, sometimes aread density profile comprises a relationship between read densities(e.g., read densities value) and genomic locations (e.g., portions,portion locations). In some embodiments, a read density profile isgenerated using a static window process, and in certain embodiments, aread density profile is generated using a sliding window process. Insome embodiments a read density profile is sometimes printed and/ordisplayed (e.g., displayed as a visual representation, e.g., a plot or agraph).

In some embodiments, a read density profile corresponds to a set ofportions (e.g., a set of portions of a reference genome, a set ofportions of a chromosome or a subset of portions of a part of achromosome). In some embodiments a read density profile comprises readdensities and/or counts associated with a collection (e.g., a set, asubset) of portions. In some embodiments, a read density profile isdetermined for read densities of portions that are contiguous. In someembodiments, contiguous portions comprise gaps comprising regions of areference sequence and/or sequence reads that are not included in adensity profile (e.g., portions removed by a filtering). Sometimesportions (e.g., a set of portions) that are contiguous representneighboring regions of a genome or neighboring regions of a chromosomeor gene. For example, two or more contiguous portions, when aligned bymerging the portions end to end, can represent a sequence assembly of aDNA sequence longer than each portion. For example two or morecontiguous portions can represent an intact genome, chromosome, gene,intron, exon or part thereof. Sometimes a read density profile isdetermined from a collection (e.g., a set, a subset) of contiguousportions and/or non-contiguous portions. In some cases, a read densityprofile comprises one or more portions, which portions can be weighted,removed, filtered, normalized, adjusted, averaged, derived as a mean,added, subtracted, processed or transformed by any combination thereof.

A read density profile is often determined for a sample and/or areference (e.g., a reference sample). A read density profile issometimes generated for an entire genome, one or more chromosomes, orfor a part of a genome or a chromosome. In some embodiments, one or moreread density profiles are determined for a genome or part thereof. Insome embodiments, a read density profile is representative of theentirety of a set of read densities of a sample, and in certainembodiments, a read density profile is representative of a part orsubset of read densities of a sample. That is, sometimes a read densityprofile comprises or is generated from read densities representative ofdata that has not been filtered to remove any data, and sometimes a readdensity profile includes or is generated from data points representativeof data that has been filtered to remove unwanted data.

In some embodiments a read density profile is determined for a reference(e.g., a reference sample, a training set). A read density profile for areference is sometimes referred to herein as a reference profile.

In some embodiments a reference profile comprises a read densitiesobtained from one or more references (e.g., reference sequences,reference samples). In some embodiments a reference profile comprisesread densities determined for one or more (e.g., a set of) known euploidsamples. In some embodiments a reference profile comprises readdensities of filtered portions. In some embodiments a reference profilecomprises read densities adjusted according to the one or more principalcomponents.

Performing a Comparison

In some embodiments, a processing step comprises preforming a comparison(e.g., comparing a test profile to a reference profile). Two or moredata sets, two or more relationships and/or two or more profiles can becompared by a suitable method. Non-limiting examples of statisticalmethods suitable for comparing data sets, relationships and/or profilesinclude Behrens-Fisher approach, bootstrapping, Fisher's method forcombining independent tests of significance, Neyman-Pearson testing,confirmatory data analysis, exploratory data analysis, exact test,F-test, Z-test, T-test, calculating and/or comparing a measure ofuncertainty, a null hypothesis, counternulls and the like, a chi-squaretest, omnibus test, calculating and/or comparing level of significance(e.g., statistical significance), a meta analysis, a multivariateanalysis, a regression, simple linear regression, robust linearregression, the like or combinations of the foregoing. In certainembodiments comparing two or more data sets, relationships and/orprofiles comprises determining and/or comparing a measure ofuncertainty. A “measure of uncertainty” as used herein refers to ameasure of significance (e.g., statistical significance), a measure oferror, a measure of variance, a measure of confidence, the like or acombination thereof A measure of uncertainty can be a value (e.g., athreshold) or a range of values (e.g., an interval, a confidenceinterval, a Bayesian confidence interval, a threshold range).Non-limiting examples of a measure of uncertainty include p-values, asuitable measure of deviation (e.g., standard deviation, sigma, absolutedeviation, mean absolute deviation, the like), a suitable measure oferror (e.g., standard error, mean squared error, root mean squarederror, the like), a suitable measure of variance, a suitable standardscore (e.g., standard deviations, cumulative percentages, percentileequivalents, Z-scores, T-scores, R-scores, standard nine (stanine),percent in stanine, the like), the like or combinations thereof. In someembodiments determining the level of significance comprises determininga measure of uncertainty (e.g., a p-value). In certain embodiments, twoor more data sets, relationships and/or profiles can be analyzed and/orcompared by utilizing multiple (e.g., 2 or more) statistical methods(e.g., least squares regression, principal component analysis, lineardiscriminant analysis, quadratic discriminant analysis, bagging, neuralnetworks, support vector machine models, random forests, classificationtree models, K-nearest neighbors, logistic regression and/or losssmoothing) and/or any suitable mathematical and/or statisticalmanipulations (e.g., referred to herein as manipulations).

In some embodiments, a processing step comprises a comparison of two ormore profiles (e.g., two or more read density profiles). Comparingprofiles may comprise comparing profiles generated for a selected regionof a genome. For example, a test profile may be compared to a referenceprofile where the test and reference profiles were determined for aregion of a genome (e.g., a reference genome) that is substantially thesame region. Comparing profiles sometimes comprises comparing two ormore subsets of portions of a profile (e.g., a read density profile). Asubset of portions of a profile may represent a region of a genome(e.g., a chromosome, or region thereof). A profile (e.g., a read densityprofile) can comprise any amount of subsets of portions. Sometimes aprofile (e.g., a read density profile) comprises two or more, three ormore, four or more, or five or more subsets. In certain embodiments, aprofile (e.g., a read density profile) comprises two subsets of portionswhere each portion represents regions of a reference genome that areadjacent. In some embodiments, a test profile can be compared to areference profile where the test profile and reference profile bothcomprise a first subset of portions and a second subset of portionswhere the first and second subsets represent different regions of agenome. Some subsets of portions of a profile may comprise copy numberalterations and other subsets of portions are sometimes substantiallyfree of copy number alterations. Sometimes all subsets of portions of aprofile (e.g., a test profile) are substantially free of a copy numberalteration. Sometimes all subsets of portions of a profile (e.g., a testprofile) comprise a copy number alteration. In some embodiments a testprofile can comprise a first subset of portions that comprise a copynumber alteration and a second subset of portions that are substantiallyfree of a copy number alteration.

In certain embodiments, comparing two or more profiles comprisesdetermining and/or comparing a measure of uncertainty for two or moreprofiles. Profiles (e.g., read density profiles) and/or associatedmeasures of uncertainty are sometimes compared to facilitateinterpretation of mathematical and/or statistical manipulations of adata set and/or to provide an outcome. A profile (e.g., a read densityprofile) generated for a test subject sometimes is compared to a profile(e.g., a read density profile) generated for one or more references(e.g., reference samples, reference subjects, and the like). In someembodiments, an outcome is provided by comparing a profile (e.g., a readdensity profile) from a test subject to a profile (e.g., a read densityprofile) from a reference for a chromosome, portions or parts thereof,where a reference profile is obtained from a set of reference subjectsknown not to possess a copy number alteration (e.g., a reference). Insome embodiments an outcome is provided by comparing a profile (e.g., aread density profile) from a test subject to a profile (e.g., a readdensity profile) from a reference for a chromosome, portions or partsthereof, where a reference profile is obtained from a set of referencesubjects known to possess a specific copy number alteration (e.g., achromosome aneuploidy, a microduplication, a microdeletion).

In certain embodiments, a profile (e.g., a read density profile) of atest subject is compared to a predetermined value representative of theabsence of a copy number alteration, and sometimes deviates from apredetermined value at one or more genomic locations (e.g., portions)corresponding to a genomic location in which a copy number alteration islocated. For example, in test subjects (e.g., subjects at risk for, orsuffering from a medical condition associated with a copy numberalteration), profiles are expected to differ significantly from profilesof a reference (e.g., a reference sequence, reference subject, referenceset) for selected portions when a test subject comprises a copy numberalteration in question. Profiles (e.g., read density profiles) of a testsubject are often substantially the same as profiles (e.g., read densityprofiles) of a reference (e.g., a reference sequence, reference subject,reference set) for selected portions when a test subject does notcomprise a copy number alteration in question. Profiles (e.g., readdensity profiles) may be compared to a predetermined threshold and/orthreshold range. The term “threshold” as used herein refers to anynumber that is calculated using a qualifying data set and serves as alimit of diagnosis of a copy number alteration (e.g., an aneuploidy, amicroduplication, a microdeletion, and the like). In certain embodimentsa threshold is exceeded by results obtained by methods described hereinand a subject is diagnosed with a copy number alteration. In someembodiments, a threshold value or range of values may be calculated bymathematically and/or statistically manipulating sequence read data(e.g., from a reference and/or subject). A predetermined threshold orthreshold range of values indicative of the presence or absence of acopy number alteration can vary while still providing an outcome usefulfor determining the presence or absence of a copy number alteration. Incertain embodiments, a profile (e.g., a read density profile) comprisingnormalized read densities and/or normalized counts is generated tofacilitate classification and/or providing an outcome. An outcome can beprovided based on a plot of a profile (e.g., a read density profile)comprising normalized counts (e.g., using a plot of such a read densityprofile).

Decision Analysis

In some embodiments, a determination of an outcome (e.g., making a call)or a determination of the presence or absence of a copy numberalteration (e.g., chromosome aneuploidy, microduplication,microdeletion) is made according to a decision analysis. Certaindecision analysis features are described in International PatentApplication Publication No. WO2014/190286, which is incorporated byreference herein. For example, a decision analysis sometimes comprisesapplying one or more methods that produce one or more results, anevaluation of the results, and a series of decisions based on theresults, evaluations and/or the possible consequences of the decisionsand terminating at some juncture of the process where a final decisionis made. In some embodiments a decision analysis is a decision tree. Adecision analysis, in some embodiments, comprises coordinated use of oneor more processes (e.g., process steps, e.g., algorithms). A decisionanalysis can be performed by a person, a system, an apparatus, software(e.g., a module), a computer, a processor (e.g., a microprocessor), thelike or a combination thereof In some embodiments a decision analysiscomprises a method of determining the presence or absence of a copynumber alteration (e.g., chromosome aneuploidy, microduplication ormicrodeletion) with reduced false negative and reduced false positivedeterminations, compared to an instance in which no decision analysis isutilized (e.g., a determination is made directly from normalizedcounts). In some embodiments a decision analysis comprises determiningthe presence or absence of a condition associated with one or more copynumber alterations.

In some embodiments a decision analysis comprises generating a profilefor a genome or a region of a genome (e.g., a chromosome or partthereof). A profile can be generated by any suitable method, known ordescribed herein. In some embodiments, a decision analysis comprises asegmenting process. Segmenting can modify and/or transform a profilethereby providing one or more decomposition renderings of a profile. Aprofile subjected to a segmenting process often is a profile ofnormalized counts mapped to portions in a reference genome or partthereof. As addressed herein, raw counts mapped to the portions can benormalized by one or more suitable normalization processes (e.g., LOESS,GC-LOESS, principal component normalization, or combination thereof) togenerate a profile that is segmented as part of a decision analysis. Adecomposition rendering of a profile is often a transformation of aprofile. A decomposition rendering of a profile is sometimes atransformation of a profile into a representation of a genome,chromosome or part thereof.

In certain embodiments, a segmenting process utilized for the segmentinglocates and identifies one or more levels within a profile that aredifferent (e.g., substantially or significantly different) than one ormore other levels within a profile. A level identified in a profileaccording to a segmenting process that is different than another levelin the profile, and has edges that are different than another level inthe profile, is referred to herein as a level for a discrete segment. Asegmenting process can generate, from a profile of normalized counts orlevels, a decomposition rendering in which one or more discrete segmentscan be identified. A discrete segment generally covers fewer portionsthan what is segmented (e.g., chromosome, chromosomes, autosomes).

In some embodiments, segmenting locates and identifies edges of discretesegments within a profile. In certain embodiments, one or both edges ofone or more discrete segments are identified. For example, asegmentation process can identify the location (e.g., genomiccoordinates, e.g., portion location) of the right and/or the left edgesof a discrete segment in a profile. A discrete segment often comprisestwo edges. For example, a discrete segment can include a left edge and aright edge. In some embodiments, depending upon the representation orview, a left edge can be a 5′-edge and a right edge can be a 3′-edge ofa nucleic acid segment in a profile. In some embodiments, a left edgecan be a 3′-edge and a right edge can be a 5′-edge of a nucleic acidsegment in a profile. Often the edges of a profile are known prior tosegmentation and therefore, in some embodiments, the edges of a profiledetermine which edge of a level is a 5′-edge and which edge is 3′-edge.In some embodiments one or both edges of a profile and/or discretesegment is an edge of a chromosome.

In some embodiments, the edges of a discrete segment are determinedaccording to a decomposition rendering generated for a reference sample(e.g., a reference profile). In some embodiments a null edge heightdistribution is determined according to a decomposition rendering of areference profile (e.g., a profile of a chromosome or part thereof). Incertain embodiments, the edges of a discrete segment in a profile areidentified when the level of the discrete segment is outside a null edgeheight distribution. In some embodiments, the edges of a discretesegment in a profile are identified according a Z-score calculatedaccording to a decomposition rendering for a reference profile.

In some instances, segmenting generates two or more discrete segments(e.g., two or more fragmented levels, two or more fragmented segments)in a profile. In some embodiments, a decomposition rendering derivedfrom a segmenting process is over-segmented or fragmented and comprisesmultiple discrete segments. Sometimes discrete segments generated bysegmenting are substantially different and sometimes discrete segmentsgenerated by segmenting are substantially similar. Substantially similardiscrete segments (e.g., substantially similar levels) often refers totwo or more adjacent discrete segments in a segmented profile eachhaving a level that differs by less than a predetermined level ofuncertainty. In some embodiments, substantially similar discretesegments are adjacent to each other and are not separated by anintervening segment. In some embodiments, substantially similar discretesegments are separated by one or more smaller segments. In someembodiments substantially similar discrete segments are separated byabout 1 to about 20, about 1 to about 15, about 1 to about 10 or about 1to about 5 portions where one or more of the intervening portions have alevel significantly different than the level of each of thesubstantially similar discrete segments. In some embodiments, the levelof substantially similar discrete segments differs by less than about 3times, less than about 2 times, less than about 1 time or less thanabout 0.5 times a level of uncertainty. Substantially similar discretesegments, in some embodiments, comprise a median level that differs byless than 3 MAD (e.g., less than 3 sigma), less than 2 MAD, less than 1MAD or less than about 0.5 MAD, where a MAD is calculated from a medianlevel of each of the segments. Substantially different discretesegments, in some embodiments, are not adjacent or are separated by 10or more, 15 or more or 20 or more portions. Substantially differentdiscrete segments generally have substantially different levels. Incertain embodiments, substantially different discrete segments compriseslevels that differ by more than about 2.5 times, more than about 3times, more than about 4 times, more than about 5 times, more than about6 times a level of uncertainty. Substantially different discretesegments, in some embodiments, comprise a median level that differs bymore than 2.5 MAD (e.g., more than 2.5 sigma), more than 3 MAD, morethan 4 MAD, more than about 5 MAD or more than about 6 MAD, where a MADis calculated from a median level of each of the discrete segments.

In some embodiments, a segmentation process comprises determining (e.g.,calculating) a level (e.g., a quantitative value, e.g., a mean or medianlevel), a level of uncertainty (e.g., an uncertainty value), Z-score,Z-value, p-value, the like or combinations thereof for one or morediscrete segments in a profile or part thereof. In some embodiments alevel (e.g., a quantitative value, e.g., a mean or median level), alevel of uncertainty (e.g., an uncertainty value), Z-score, Z-value,p-value, the like or combinations thereof are determined (e.g.,calculated) for a discrete segment.

Segmenting can be performed, in full or in part, by one or moredecomposition generating processes. A decomposition generating processmay provide, for example, a decomposition rendering of a profile. Anydecomposition generating process described herein or known in the artmay be used. Non-limiting examples of a decomposition generating processinclude circular binary segmentation (CBS) (see e.g., Olshen et al.(2004) Biostatistics 5(4):557-72; Venkatraman, E S, Olshen, A B (2007)Bioinformatics 23(6):657-63); Haar wavelet segmentation (see e.g., Haar,Alfred (1910) Mathematische Annalen 69(3):331-371); maximal overlapdiscrete wavelet transform (MODWT) (see e.g., Hsu et al. (2005)Biostatistics 6 (2):211-226); stationary wavelet (SWT) (see e.g., Y.Wang and S. Wang (2007) International Journal of Bioinformatics Researchand Applications 3(2):206-222); dual-tree complex wavelet transform(DTCWT) (see e.g., Nguyen et al. (2007) Proceedings of the 7th IEEEInternational Conference, Boston Mass., on Oct. 14-17, 2007, pages137-144); maximum entropy segmentation, convolution with edge detectionkernel, Jensen Shannon Divergence, Kullback-Leibler divergence, BinaryRecursive Segmentation, a Fourier transform, the like or combinationsthereof.

In some embodiments, segmenting is accomplished by a process thatcomprises one process or multiple sub-processes, non-limiting examplesof which include a decomposition generating process, thresholding,leveling, smoothing, polishing, the like or combination thereof.Thresholding, leveling, smoothing, polishing and the like can beperformed in conjunction with a decomposition generating process, forexample.

In some embodiments, a decision analysis comprises identifying acandidate segment in a decomposition rendering. A candidate segment isdetermined as being the most significant discrete segment in adecomposition rendering. A candidate segment may be the most significantin terms of the number of portions covered by the segment and/or interms of the absolute value of the level of normalized counts for thesegment. A candidate segment sometimes is larger and sometimessubstantially larger than other discrete segments in a decompositionrendering. A candidate segment can be identified by a suitable method.In some embodiments, a candidate segment is identified by an area underthe curve (AUC) analysis. In certain embodiments, where a first discretesegment has a level and/or covers a number of portions substantiallylarger than for another discrete segment in a decomposition rendering,the first segment comprises a larger AUC. Where a level is analyzed forAUC, an absolute value of a level often is utilized (e.g., a levelcorresponding to normalized counts can have a negative value for adeletion and a positive value for a duplication). In certainembodiments, an AUC is determined as an absolute value of a calculatedAUC (e.g., a resulting positive value). In certain embodiments, acandidate segment, once identified (e.g., by an AUC analysis or by asuitable method) and optionally after it is validated, is selected for az-score calculation, or the like, to determine if the candidate segmentrepresents a genetic variation or genetic alteration (e.g., ananeuploidy, microdeletion or microduplication).

In some embodiments, a decision analysis comprises a comparison. In someembodiments, a comparison comprises comparing at least two decompositionrenderings. In some embodiments, a comparison comprises comparing atleast two candidate segments. In certain embodiments, each of the atleast two candidate segments is from a different decompositionrendering. For example, a first candidate segment can be from a firstdecomposition rendering and a second candidate segment can be from asecond decomposition rendering. In some embodiments, a comparisoncomprises determining if two decomposition renderings are substantiallythe same or different. In some embodiments, a comparison comprisesdetermining if two candidate segments are substantially the same ordifferent. Two candidate segments can be determined as substantially thesame or different by a suitable comparison method, non-limiting examplesof which include by visual inspection, by comparing levels or Z-scoresof the two candidate segments, by comparing the edges of the twocandidate segments, by overlaying either the two candidate segments ortheir corresponding decomposition renderings, the like or combinationsthereof.

Classifications and Uses Thereof

Methods described herein can provide an outcome indicative of a genotypeand/or presence or absence of a genetic variation/alteration in agenomic region for a test sample (e.g., providing an outcomedeterminative of the presence or absence of a genetic variation).Methods described herein sometimes provide an outcome indicative of aphenotype and/or presence or absence of a medical condition for a testsample (e.g., providing an outcome determinative of the presence orabsence of a medical condition and/or phenotype). An outcome often ispart of a classification process, and a classification (e.g.,classification of presence or absence of a genotype, phenotype, geneticvariation and/or medical condition for a test sample) sometimes is basedon and/or includes an outcome. An outcome and/or classificationsometimes is based on and/or includes a result of data processing for atest sample that facilitates determining presence or absence of agenotype, phenotype, genetic variation, genetic alteration, and/ormedical condition in a classification process (e.g., a statistic value(e.g., standard score (e.g., z-score)). An outcome and/or classificationsometimes includes or is based on a score determinative of, or a callof, presence or absence of a genotype, phenotype, genetic variation,genetic alteration, and/or medical condition. In certain embodiments, anoutcome and/or classification includes a conclusion that predicts and/ordetermines presence or absence of a genotype, phenotype, geneticvariation, genetic alteration, and/or medical condition in aclassification process.

A genotype and/or genetic variation often includes a gain, a loss and/oralteration of a region comprising one or more nucleotides (e.g.,duplication, deletion, fusion, insertion, short tandem repeat (STR),mutation, single nucleotide alteration, reorganization, substitution oraberrant methylation) that results in a detectable change in the genomeor genetic information for a test sample. A genotype and/or geneticvariation often is in a particular genomic region (e.g., chromosome,portion of a chromosome (i.e., sub-chromosome region), STR, polymorphicregion, translocated region, altered nucleotide sequence, the like orcombinations of the foregoing). A genetic variation sometimes is a copynumber alteration for a particular region, such as a trisomy or monosomyfor chromosome region, or a microduplication or microdeletion event fora particular region (e.g., gain or loss of a region of about 10megabases or less (e.g., about 9 megabases or less, 8 megabases or less,7 megabases or less, 6 megabases or less, 5 megabases or less, 4megabases or less, 3 megabases or less, 2 megabases or less or 1megabase or less)), for example. A copy number alteration sometimes isexpressed as having no copy or one, two, three or four or more copies ofa particular region (e.g., chromosome, sub-chromosome, STR,microduplication or microdeletion region).

Presence or absence of a genotype, phenotype, genetic variation and/ormedical condition can be determined by transforming, analyzing and/ormanipulating sequence reads that have been mapped to genomic portions(e.g., counts, counts of genomic portions of a reference genome). Incertain embodiments, an outcome and/or classification is determinedaccording to normalized counts, read densities, read density profiles,and the like, and can be determined by a method described herein. Anoutcome and/or classification sometimes includes one or more scoresand/or calls that refer to the probability that a particular genotype,phenotype, genetic variation, or medical condition is present or absentfor a test sample. The value of a score may be used to determine, forexample, a variation, difference, or ratio of mapped sequence reads thatmay correspond to a genotype, phenotype, genetic variation, or medicalcondition. For example, calculating a positive score for a selectedgenotype, phenotype, genetic variation, or medical condition from a dataset, with respect to a reference genome, can lead to a classification ofthe genotype, phenotype, genetic variation, or medical condition, for atest sample.

Any suitable expression of an outcome and/or classification can beprovided. An outcome and/or classification sometimes is based on and/orincludes one or more numerical values generated using a processingmethod described herein in the context of one or more considerations ofprobability. Non-limiting examples of values that can be utilizedinclude a sensitivity, specificity, standard deviation, median absolutedeviation (MAD), measure of certainty, measure of confidence, measure ofcertainty or confidence that a value obtained for a test sample isinside or outside a particular range of values, measure of uncertainty,measure of uncertainty that a value obtained for a test sample is insideor outside a particular range of values, coefficient of variation (CV),confidence level, confidence interval (e.g., about 95% confidenceinterval), standard score (e.g., z-score), chi value, phi value, resultof a t-test, p-value, ploidy value, fitted minority species fraction,area ratio, median level, the like or combination thereof. In someembodiments, an outcome and/or classification comprises a read density,a read density profile and/or a plot (e.g., a profile plot). In certainembodiments, multiple values are analyzed together, sometimes in aprofile for such values (e.g., z-score profile, p-value profile, chivalue profile, phi value profile, result of a t-test, value profile, thelike, or combination thereof). A consideration of probability canfacilitate determining whether a subject is at risk of having, or has, agenotype, phenotype, genetic variation and/or medical condition, and anoutcome and/or classification determinative of the foregoing sometimesincludes such a consideration.

In certain embodiments, an outcome and/or classification is based onand/or includes a conclusion that predicts and/or determines a risk orprobability of the presence or absence of a genotype, phenotype, geneticvariation and/or medical condition for a test sample. A conclusionsometimes is based on a value determined from a data analysis methoddescribed herein (e.g., a statistics value indicative of probability,certainty and/or uncertainty (e.g., standard deviation, median absolutedeviation (MAD), measure of certainty, measure of confidence, measure ofcertainty or confidence that a value obtained for a test sample isinside or outside a particular range of values, measure of uncertainty,measure of uncertainty that a value obtained for a test sample is insideor outside a particular range of values, coefficient of variation (CV),confidence level, confidence interval (e.g., about 95% confidenceinterval), standard score (e.g., z-score), chi value, phi value, resultof a t-test, p-value, sensitivity, specificity, the like or combinationthereof). An outcome and/or classification sometimes is expressed in alaboratory test report (described in greater detail hereafter) forparticular test sample as a probability (e.g., odds ratio, p-value),likelihood, or risk factor, associated with the presence or absence of agenotype, phenotype, genetic variation and/or medical condition. Anoutcome and/or classification for a test sample sometimes is provided as“positive” or “negative” with respect a particular genotype, phenotype,genetic variation and/or medical condition. For example, an outcomeand/or classification sometimes is designated as “positive” in alaboratory test report for a particular test sample where presence of agenotype, phenotype, genetic variation and/or medical condition isdetermined, and sometimes an outcome and/or classification is designatedas “negative” in a laboratory test report for a particular test samplewhere absence of a genotype, phenotype, genetic variation and/or medicalcondition is determined. An outcome and/or classification sometimes isdetermined and sometimes includes an assumption used in data processing.

An outcome and/or classification sometimes is based on or is expressedas a value in or out of a cluster, value over or under a thresholdvalue, value within a range (e.g., a threshold range), and/or a valuewith a measure of variance or confidence. In some embodiments, anoutcome and/or classification is based on or is expressed as a valueabove or below a predetermined threshold or cutoff value and/or ameasure of uncertainty, confidence level or confidence intervalassociated with the value. In certain embodiments, a predeterminedthreshold or cutoff value is an expected level or an expected levelrange. In some embodiments, a value obtained for a test sample is astandard score (e.g., z-score), where presence of a genotype, phenotype,genetic variation and/or medical condition is determined when theabsolute value of the score is greater than a particular score threshold(e.g., threshold between about 2 and about 5; between about 3 and about4), and where the absence of a genotype, phenotype, genetic variationand/or medical condition is determined when the absolute value of thescore is less than the particular score threshold. In certainembodiments, an outcome and/or classification is based on or isexpressed as a value that falls within or outside a predetermined rangeof values (e.g., a threshold range) and the associated uncertainty orconfidence level for that value being inside or outside the range. Insome embodiments, an outcome and/or classification comprises a valuethat is equal to a predetermined value (e.g., equal to 1, equal tozero), or is equal to a value within a predetermined value range, andits associated uncertainty or confidence level for that value beingequal or within or outside the range. An outcome and/or classificationsometimes is graphically represented as a plot (e.g., profile plot). Anoutcome and/or classification sometimes comprises use of a referencevalue or reference profile, and sometimes a reference value or referenceprofile is obtained from one or more reference samples (e.g., referencesample(s) euploid for a selected part of a genome (e.g., region)).

In some embodiments, an outcome and/or classification is based on orincludes use of a measure of uncertainty between a test value or profileand a reference value or profile for a selected region. In someembodiments, a determination of the presence or absence of a genotype,phenotype, genetic variation and/or medical condition is according tothe number of deviations (e.g., sigma) between a test value or profileand a reference value or profile for a selected region (e.g., achromosome, or part thereof). A measure of deviation often is anabsolute value or absolute measure of deviation (e.g., mean absolutedeviation or median absolute deviation (MAD)). In some embodiments, thepresence of a genotype, phenotype, genetic variation and/or medicalcondition is determined when the number of deviations between a testvalue or profile and a reference value or profile is about 1 or greater(e.g., about 1.5, 2, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4,3.5, 3.6, 3.7, 3.8, 3.9, 4, 5 or 6 deviations or greater). In certainembodiments, presence of a genotype, phenotype, genetic variation and/ormedical condition is determined when a test value or profile and areference value or profile differ by about 2 to about 5 measures ofdeviation (e.g., sigma, MAD), or more than 3 measures of deviation(e.g., 3 sigma, 3 MAD). A deviation of greater than three between a testvalue or profile and a reference value or profile often is indicative ofa non-euploid test subject (e.g., presence of a genetic variation (e.g.,presence of trisomy, monosomy, microduplication, microdeletion) for aselected region. Test values or profiles significantly above a referenceprofile, which reference profile is indicative of euploidy, sometimesare determinative of a trisomy, sub-chromosome duplication ormicroduplication. Test values or profiles significantly below areference profile, which reference profile is indicative of euploidy,sometimes are determinative of a monosomy, sub-chromosome deletion ormicrodeletion. In some embodiments, absence of a genotype, phenotype,genetic variation and/or medical condition is determined when the numberof deviations between a test value or profile and reference value orprofile for a selected region of a genome is about 3.5 or less (e.g.,about less than about 3.4, 3.3, 3.2, 3.1, 3, 2.9, 2.8, 2.7, 2.6, 2.5,2.4, 2.3, 2.2, 2.1, 2, 1.9, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3, 1.2, 1.1, 1 orless). In certain embodiments, absence of a genotype, phenotype, geneticvariation and/or medical condition is determined when a test value orprofile differs from a reference value or profile by less than threemeasures of deviation (e.g., 3 sigma, 3 MAD). In some embodiments, ameasure of deviation of less than three between a test value or profileand reference value or profile (e.g., 3-sigma for standard deviation)often is indicative of a region that is euploid (e.g., absence of agenetic variation). A measure of deviation between a test value orprofile for a test sample and a reference value or profile for one ormore reference subjects can be plotted and visualized (e.g., z-scoreplot).

In some embodiments, an outcome and/or classification is determinedaccording to a call zone. In certain embodiments, a call is made (e.g.,a call determining presence or absence of a genotype, phenotype, geneticvariation and/or medical condition) when a value (e.g., a profile, aread density profile and/or a measure of uncertainty) or collection ofvalues falls within a pre-defined range (e.g., a zone, a call zone). Insome embodiments, a call zone is defined according to a collection ofvalues (e.g., profiles, read density profiles, measures or determinationof probability and/or measures of uncertainty) obtained from aparticular group of samples. In certain embodiments, a call zone isdefined according to a collection of values that are derived from thesame chromosome or part thereof. In some embodiments, a call zone fordetermining presence or absence of a genotype, phenotype, geneticvariation and/or medical condition is defined according a measure ofuncertainty (e.g., high level of confidence or low measure ofuncertainty) and/or a quantification of a minority nucleic acid species(e.g., about 1% minority species or greater (e.g., about 2, 3, 4, 5, 6,7, 8, 9, 10% or more minority nucleic acid species)) determined for atest sample. A minority nucleic acid species quantification sometimes isa fraction or percent of cancer cell nucleic acid or fetal nucleic acid(i.e., fetal fraction) ascertained for a test sample. In someembodiments, a call zone is defined by a confidence level or confidenceinterval (e.g., a confidence interval for 95% level of confidence). Acall zone sometimes is defined by a confidence level, or confidenceinterval based on a particular confidence level, of about 90% or greater(e.g., about 91, 92, 93, 94, 95, 96, 97, 98, 99, 99.1, 99.2, 99.3, 99.4,99.5, 99.6, 99.7, 99.8, 99.9% or greater). In some embodiments, a callis made using a call zone and additional data or information. In someembodiments, a call is made without using a call zone. In someembodiments, a call is made based on a comparison without the use of acall zone. In some embodiments, a call is made based on visualinspection of a profile (e.g., visual inspection of read densities).

In some embodiments, a classification or call is not provided for a testsample when a test value or profile is in a no-call zone. In someembodiments, a no-call zone is defined by a value (e.g., collection ofvalues) or profile that indicates low accuracy, high risk, high error,low level of confidence, high measure of uncertainty, the like orcombination thereof In some embodiments, a no-call zone is defined, inpart, by a minority nucleic acid species quantification (e.g., aminority nucleic acid species of about 10% or less (e.g., about 9, 8, 7,6, 5, 4, 3, 2% or less minority nucleic acid species)). An outcomeand/or classification generated for determining the presence or absenceof a genotype, phenotype, genetic variation and/or medical conditionsometimes includes a null result. A null result sometimes is a datapoint between two clusters, a numerical value with a standard deviationthat encompasses values for both the presence and absence of a genotype,phenotype, genetic variation and/or medical condition, a data set with aprofile plot that is not similar to profile plots for subjects having orfree from the genetic variation being investigated). In someembodiments, an outcome and/or classification indicative of a nullresult is considered a determinative result, and the determination caninclude a conclusion of the need for additional information and/or arepeat of data generation and/or analysis for determining the presenceor absence of a genotype, phenotype, genetic variation and/or medicalcondition.

There typically are four types of classifications generated in aclassification process: true positive, false positive, true negative andfalse negative. The term “true positive” as used herein refers topresence of a genotype, phenotype, genetic variation, or medicalcondition correctly determined for a test sample. The term “falsepositive” as used herein refers to presence of a genotype, phenotype,genetic variation, or medical condition incorrectly determined for atest sample. The term “true negative” as used herein refers to absenceof a genotype, phenotype, genetic variation, or medical conditioncorrectly determined for a test sample. The term “false negative” asused herein refers to absence of a genotype, phenotype, geneticvariation, or medical condition incorrectly determined for a testsample. Two measures of performance for a classification process can becalculated based on the ratios of these occurrences: (i) a sensitivityvalue, which generally is the fraction of predicted positives that arecorrectly identified as being positives; and (ii) a specificity value,which generally is the fraction of predicted negatives correctlyidentified as being negative.

In certain embodiments, a laboratory test report generated for aclassification process includes a measure of test performance (e.g.,sensitivity and/or specificity) and/or a measure of confidence (e.g., aconfidence level, confidence interval). A measure of test performanceand/or confidence sometimes is obtained from a clinical validation studyperformed prior to performing a laboratory test for a test sample. Incertain embodiments, one or more of sensitivity, specificity and/orconfidence are expressed as a percentage. In some embodiments, apercentage expressed independently for each of sensitivity, specificityor confidence level, is greater than about 90% (e.g., about 90, 91, 92,93, 94, 95, 96, 97, 98 or 99%, or greater than 99% (e.g., about 99.5%,or greater, about 99.9% or greater, about 99.95% or greater, about99.99% or greater)). A confidence interval expressed for a particularconfidence level (e.g., a confidence level of about 90% to about 99.9%(e.g., about 95%)) can be expressed as a range of values, and sometimesis expressed as a range or sensitivities and/or specificities for aparticular confidence level. Coefficient of variation (CV) in someembodiments is expressed as a percentage, and sometimes the percentageis about 10% or less (e.g., about 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1%, orless than 1% (e.g., about 0.5% or less, about 0.1% or less, about 0.05%or less, about 0.01% or less)). A probability (e.g., that a particularoutcome and/or classification is not due to chance) in certainembodiments is expressed as a standard score (e.g., z-score), a p-value,or result of a t-test. In some embodiments, a measured variance,confidence level, confidence interval, sensitivity, specificity and thelike (e.g., referred to collectively as confidence parameters) for anoutcome and/or classification can be generated using one or more dataprocessing manipulations described herein. Specific examples ofgenerating an outcome and/or classification and associated confidencelevels are described, for example, in International Patent ApplicationPublication Nos. WO2013/052913, WO2014/190286 and WO2015/051163, theentire content of which is incorporated herein by reference, includingall text, tables, equations and drawings.

An outcome and/or classification for a test sample often is ordered by,and often is provided to, a health care professional or other qualifiedindividual (e.g., physician or assistant) who transmits an outcomeand/or classification to a subject from whom the test sample isobtained. In certain embodiments, an outcome and/or classification isprovided using a suitable visual medium (e.g., a peripheral or componentof a machine, e.g., a printer or display). A classification and/oroutcome often is provided to a healthcare professional or qualifiedindividual in the form of a report. A report typically comprises adisplay of an outcome and/or classification (e.g., a value, or anassessment or probability of presence or absence of a genotype,phenotype, genetic variation and/or medical condition), sometimesincludes an associated confidence parameter, and sometimes includes ameasure of performance for a test used to generate the outcome and/orclassification. A report sometimes includes a recommendation for afollow-up procedure (e.g., a procedure that confirms the outcome orclassification). A report sometimes includes a visual representation ofa chromosome or portion thereof (e.g., a chromosome ideogram orkaryogram), and sometimes shows a visualization of a duplication and/ordeletion region for a chromosome (e.g., a visualization of a wholechromosome for a chromosome deletion or duplication; a visualization ofa whole chromosome with a deleted region or duplicated region shown; avisualization of a portion of chromosome duplicated or deleted; avisualization of a portion of a chromosome remaining in the event of adeletion of a portion of a chromosome) identified for a test sample.

A report can be displayed in a suitable format that facilitatesdetermination of presence or absence of a genotype, phenotype, geneticvariation and/or medical condition by a health professional or otherqualified individual. Non-limiting examples of formats suitable for usefor generating a report include digital data, a graph, a 2D graph, a 3Dgraph, and 4D graph, a picture (e.g., a jpg, bitmap (e.g., bmp), pdf,tiff, gif, raw, png, the like or suitable format), a pictograph, achart, a table, a bar graph, a pie graph, a diagram, a flow chart, ascatter plot, a map, a histogram, a density chart, a function graph, acircuit diagram, a block diagram, a bubble map, a constellation diagram,a contour diagram, a cartogram, spider chart, Venn diagram, nomogram,and the like, or combination of the foregoing.

A report may be generated by a computer and/or by human data entry, andcan be transmitted and communicated using a suitable electronic medium(e.g., via the internet, via computer, via facsimile, from one networklocation to another location at the same or different physical sites),or by another method of sending or receiving data (e.g., mail service,courier service and the like). Non-limiting examples of communicationmedia for transmitting a report include auditory file, computer readablefile (e.g., pdf file), paper file, laboratory file, medical record file,or any other medium described in the previous paragraph. A laboratoryfile or medical record file may be in tangible form or electronic form(e.g., computer readable form), in certain embodiments. After a reportis generated and transmitted, a report can be received by obtaining, viaa suitable communication medium, a written and/or graphicalrepresentation comprising an outcome and/or classification, which uponreview allows a healthcare professional or other qualified individual tomake a determination as to presence or absence of a genotype, phenotype,genetic variation and/or or medical condition for a test sample.

An outcome and/or classification may be provided by and obtained from alaboratory (e.g., obtained from a laboratory file). A laboratory filecan be generated by a laboratory that carries out one or more tests fordetermining presence or absence of a genotype, phenotype, geneticvariation and/or medical condition for a test sample. Laboratorypersonnel (e.g., a laboratory manager) can analyze informationassociated with test samples (e.g., test profiles, reference profiles,test values, reference values, level of deviation, patient information)underlying an outcome and/or classification. For calls pertaining topresence or absence of a genotype, phenotype, genetic variation and/ormedical condition that are close or questionable, laboratory personnelcan re-run the same procedure using the same (e.g., aliquot of the samesample) or different test sample from a test subject. A laboratory maybe in the same location or different location (e.g., in another country)as personnel assessing the presence or absence of a genotype, phenotype,genetic variation and/or a medical condition from the laboratory file.For example, a laboratory file can be generated in one location andtransmitted to another location in which the information for a testsample therein is assessed by a healthcare professional or otherqualified individual, and optionally, transmitted to the subject fromwhich the test sample was obtained. A laboratory sometimes generatesand/or transmits a laboratory report containing a classification ofpresence or absence of genomic instability, a genotype, phenotype, agenetic variation and/or a medical condition for a test sample. Alaboratory generating a laboratory test report sometimes is a certifiedlaboratory, and sometimes is a laboratory certified under the ClinicalLaboratory Improvement Amendments (CLIA).

An outcome and/or classification sometimes is a component of a diagnosisfor a subject, and sometimes an outcome and/or classification isutilized and/or assessed as part of providing a diagnosis for a testsample. For example, a healthcare professional or other qualifiedindividual may analyze an outcome and/or classification and provide adiagnosis based on, or based in part on, the outcome and/orclassification. In some embodiments, determination, detection ordiagnosis of a medical condition, disease, syndrome or abnormalitycomprises use of an outcome and/or classification determinative ofpresence or absence of a genotype, phenotype, genetic variation and/ormedical condition. In some embodiments, an outcome and/or classificationbased on counted mapped sequence reads, normalized counts and/ortransformations thereof is determinative of presence or absence of agenotype and/or genetic variation. In certain embodiments, a diagnosiscomprises determining presence or absence of a condition, syndrome orabnormality. In certain instances, a diagnosis comprises a determinationof a genotype or genetic variation as the nature and/or cause of amedical condition, disease, syndrome or abnormality. Thus, providedherein are methods for diagnosing presence or absence of a genotype,phenotype, a genetic variation and/or a medical condition for a testsample according to an outcome or classification generated by methodsdescribed herein, and optionally according to generating andtransmitting a laboratory report that includes a classification forpresence or absence of the genotype, phenotype, a genetic variationand/or a medical condition for the test sample.

An outcome and/or classification sometimes is a component of health careand/or treatment of a subject. An outcome and/or classificationsometimes is utilized and/or assessed as part of providing a treatmentfor a subject from whom a test sample was obtained. For example, anoutcome and/or classification indicative of presence or absence of agenotype, phenotype, genetic variation, and/or medical condition is acomponent of health care and/or treatment of a subject from whom a testsample was obtained. Medical care, treatment and or diagnosis can be inany suitable area of health, such as medical treatment of subjects forprenatal care, cell proliferative conditions, cancer and the like, forexample. An outcome and/or classification determinative of presence orabsence of a genotype, phenotype, genetic variation and/or medicalcondition, disease, syndrome or abnormality by methods described hereinsometimes is independently verified by further testing. Any suitabletype of further test to verify an outcome and/or classification can beutilized, non-limiting examples of which include blood level test (e.g.,serum test), biopsy, scan (e.g., CT scan, MRI scan), invasive sampling(e.g., amniocentesis or chorionic villus sampling), karyotyping,microarray assay, ultrasound, sonogram, and the like, for example.

A healthcare professional or qualified individual can provide a suitablehealthcare recommendation based on the outcome and/or classificationprovided in a laboratory report. In some embodiments, a recommendationis dependent on the outcome and/or classification provided (e.g.,cancer, stage and/or type of cancer, Down's syndrome, Turner syndrome,medical conditions associated with genetic variations in T13, medicalconditions associated with genetic variations in T18). Non-limitingexamples of recommendations that can be provided based on an outcome orclassification in a laboratory report includes, without limitation,surgery, radiation therapy, chemotherapy, genetic counseling,after-birth treatment solutions (e.g., life planning, long term assistedcare, medicaments, symptomatic treatments), pregnancy termination, organtransplant, blood transfusion, further testing described in the previousparagraph, the like or combinations of the foregoing. Thus, methods fortreating a subject and methods for providing health care to a subjectsometimes include generating a classification for presence or absence ofa genotype, phenotype, a genetic variation and/or a medical conditionfor a test sample by a method described herein, and optionallygenerating and transmitting a laboratory report that includes aclassification of presence or absence of a genotype, phenotype, geneticvariation and/or medical condition for the test sample.

Generating an outcome and/or classification can be viewed as atransformation of nucleic acid sequence reads from a test sample into arepresentation of a subject's cellular nucleic acid. For example,transmuting sequence reads of nucleic acid from a subject by a methoddescribed herein, and generating an outcome and/or classification can beviewed as a transformation of relatively small sequence read templatesto a representation of relatively large and complex structure of nucleicacid in the subject. In some embodiments, an outcome and/orclassification results from a transformation of sequence reads from asubject into a representation of an existing nucleic acid structurepresent in the subject (e.g., a genome, a chromosome, chromosomesegment, mixture of circulating cell-free nucleic acid templates in thesubject).

In some embodiments, a method herein comprises treating a subject whenthe presence of a genetic alteration or genetic variation is determinedfor a test sample from the subject. In some embodiments, treating asubject comprises performing a medical procedure when the presence of agenetic alteration or genetic variation is determined for a test sample.In some embodiments, a medical procedure includes an invasive diagnosticprocedure such as, for example, amniocentesis, chorionic villussampling, biopsy, and the like. For example, a medical procedurecomprising amniocentesis or chorionic villus sampling may be performedwhen the presence of a fetal aneuploidy is determined for a test samplefrom a pregnant female. In another example, a medical procedurecomprising a biopsy may be performed when presence of a geneticalteration indicative of or associated with the presence of cancer isdetermined for a test sample from a subject. An invasive diagnosticprocedure may be performed to confirm a determination of the presence ofa genetic alteration or genetic variation and/or may be performed tofurther characterize a medical condition associated with a geneticalteration or genetic variation, for example. In some embodiments, amedical procedure may be performed as a treatment of a medical conditionassociated with a genetic alteration or genetic variation. Treatmentsmay include one or more of surgery, radiation therapy, chemotherapy,pregnancy termination, organ transplant, cell transplant, bloodtransfusion, medicaments, symptomatic treatments, and the like, forexample.

In some embodiments, a method herein comprises treating a subject whenthe absence of a genetic alteration or genetic variation is determinedfor a test sample from the subject. In some embodiments, treating asubject comprises performing a medical procedure when the absence of agenetic alteration or genetic variation is determined for a test sample.For example, when the absence of a genetic alteration or geneticvariation is determined for a test sample, a medical procedure mayinclude health monitoring, retesting, further screening, follow-upexaminations, and the like. In some embodiments, a method hereincomprises treating a subject consistent with a euploid pregnancy ornormal pregnancy when the absence of a fetal aneuploidy, geneticvariation or genetic alteration is determined for a test sample from apregnant female. For example, a medical procedure consistent with aeuploid pregnancy or normal pregnancy may be performed when the absenceof a fetal aneuploidy, genetic variation or genetic alteration isdetermined for a test sample from a pregnant female. A medical procedureconsistent with a euploid pregnancy or normal pregnancy may include oneor more procedures performed as part of monitoring health of the fetusand/or the mother, or monitoring feto-maternal well-being. A medicalprocedure consistent with a euploid pregnancy or normal pregnancy mayinclude one or more procedures for treating symptoms of pregnancy whichmay include, for example, one or more of nausea, fatigue, breasttenderness, frequent urination, back pain, abdominal pain, leg cramps,constipation, heartburn, shortness of breath, hemorrhoids, urinaryincontinence, varicose veins and sleeping problems. A medical procedureconsistent with a euploid pregnancy or normal pregnancy may include oneor more procedures performed throughout the course of prenatal care forassessing potential risks, treating complications, addressingpreexisting medical conditions (e.g., hypertension, diabetes), andmonitoring the growth and development of the fetus, for example. Medicalprocedures consistent with a euploid pregnancy or normal pregnancy mayinclude, for example, complete blood count (CBC) monitoring, Rh antibodytesting, urinalysis, urine culture monitoring, rubella screening,hepatitis B and hepatitis C screening, sexually transmitted infection(STI) screening (e.g., screening for syphilis, chlamydia, gonorrhea),human immunodeficiency virus (HIV) screening, tuberculosis (TB)screening, alpha-fetoprotein screening, fetal heart rate monitoring(e.g., using an ultrasound transducer), uterine activity monitoring(e.g., using toco transducer), genetic screening and/or diagnostictesting for genetic disorders (e.g., cystic fibrosis, sickle cellanemia, hemophilia A), glucose screening, glucose tolerance testing,treatment of gestational diabetes, treatment of prenatal hypertension,treatment of preeclampsia, group B streptococci (GBS) blood typescreening, group B strep culture, treatment of group B strep (e.g., withantibiotics), ultrasound monitoring (e.g., routine ultrasoundmonitoring, level II ultrasound monitoring, targeted ultrasoundmonitoring), non-stress test monitoring, biophysical profile monitoring,amniotic fluid index monitoring, serum testing (e.g., plasma protein-A(PAPP-A), alpha-fetoprotein (AFP), human chorionic gonadotropin (hCG),unconjugated estriol (uE3), and inhibin-A (inhA) testing), genetictesting, amniocentesis diagnostic testing and chorionic villus sampling(CVS) diagnostic testing.

In some embodiments, a method herein comprises treating a subjectconsistent with having no cancer when the absence of a genetic variationor genetic alteration is determined for a test sample from a subject. Incertain embodiments, a medical procedure consistent with a healthyprognosis may be performed when absence of a genetic alteration orgenetic variation associated with cancer is determined for a testsample. For example, medical procedures consistent with a healthyprognosis include without limitation monitoring health of the subjectfrom whom a test sample was tested, performing a secondary test (e.g., asecondary screening test), performing a confirmatory test, monitoringone or more biomarkers associated with cancer (e.g., prostate specificantigen (PSA) in males), monitoring blood cells (e.g., red blood cells,white blood cells, platelets), monitoring one or more vital signs (e.g.,heart rate, blood pressure), and/or monitoring one or more bloodmetabolites (e.g., total cholesterol, HDL (high-density lipoprotein),LDL (low-density lipo-protein), triglycerides, total cholesterol/HDLratio, glucose, fibrinogen, hemoglobin, dehydroepiandrosterone (DHEA),homocysteine, C-reactive protein, hormones (e.g., thyroid stimulatinghormone, testosterone, estrogen, estradiol), creatine, salt (e.g.,potassium, calcium), and the like). In some embodiments, a method hereincomprises performing no medical procedure, and sometimes no medicalprocedure that includes invasive sampling, when the absence of a geneticalteration or genetic variation is determined for a test sample.

Machines, Software and Interfaces

Certain processes and methods described herein (e.g., mapping, counting,normalizing, range setting, adjusting, categorizing and/or determiningsequence reads, counts, levels and/or profiles) often cannot beperformed without a computer, microprocessor, software, module or othermachine. Methods described herein typically are computer-implementedmethods, and one or more portions of a method sometimes are performed byone or more processors (e.g., microprocessors), computers, systems,apparatuses, or machines (e.g., microprocessor-controlled machine).

Computers, systems, apparatuses, machines and computer program productssuitable for use often include, or are utilized in conjunction with,computer readable storage media. Non-limiting examples of computerreadable storage media include memory, hard disk, CD-ROM, flash memorydevice and the like. Computer readable storage media generally arecomputer hardware, and often are non-transitory computer-readablestorage media. Computer readable storage media are not computer readabletransmission media, the latter of which are transmission signals per se.

Provided herein are computer readable storage media with an executableprogram stored thereon, where the program instructs a microprocessor toperform a method described herein. Provided also are computer readablestorage media with an executable program module stored thereon, wherethe program module instructs a microprocessor to perform part of amethod described herein. Also provided herein are systems, machines,apparatuses and computer program products that include computer readablestorage media with an executable program stored thereon, where theprogram instructs a microprocessor to perform a method described herein.Provided also are systems, machines and apparatuses that includecomputer readable storage media with an executable program module storedthereon, where the program module instructs a microprocessor to performpart of a method described herein.

Also provided are computer program products. A computer program productoften includes a computer usable medium that includes a computerreadable program code embodied therein, the computer readable programcode adapted for being executed to implement a method or part of amethod described herein. Computer usable media and readable program codeare not transmission media (i.e., transmission signals per se). Computerreadable program code often is adapted for being executed by aprocessor, computer, system, apparatus, or machine.

In some embodiments, methods described herein (e.g., quantifying,counting, filtering, normalizing, transforming, clustering and/ordetermining sequence reads, counts, levels, profiles and/or outcomes)are performed by automated methods. In some embodiments, one or moresteps of a method described herein are carried out by a microprocessorand/or computer, and/or carried out in conjunction with memory. In someembodiments, an automated method is embodied in software, modules,microprocessors, peripherals and/or a machine comprising the like, thatperform methods described herein. As used herein, software refers tocomputer readable program instructions that, when executed by amicroprocessor, perform computer operations, as described herein.

Sequence reads, counts, levels and/or profiles sometimes are referred toas “data” or “data sets.” In some embodiments, data or data sets can becharacterized by one or more features or variables (e.g., sequence based(e.g., GC content, specific nucleotide sequence, the like), functionspecific (e.g., expressed genes, cancer genes, the like), location based(genome specific, chromosome specific, portion or portion-specific), thelike and combinations thereof). In certain embodiments, data or datasets can be organized into a matrix having two or more dimensions basedon one or more features or variables. Data organized into matrices canbe organized using any suitable features or variables. In certainembodiments, data sets characterized by one or more features orvariables sometimes are processed after counting.

Machines, software and interfaces may be used to conduct methodsdescribed herein. Using machines, software and interfaces, a user mayenter, request, query or determine options for using particularinformation, programs or processes (e.g., mapping sequence reads,processing mapped data and/or providing an outcome), which can involveimplementing statistical analysis algorithms, statistical significancealgorithms, statistical algorithms, iterative steps, validationalgorithms, and graphical representations, for example. In someembodiments, a data set may be entered by a user as input information, auser may download one or more data sets by suitable hardware media(e.g., flash drive), and/or a user may send a data set from one systemto another for subsequent processing and/or providing an outcome (e.g.,send sequence read data from a sequencer to a computer system forsequence read mapping; send mapped sequence data to a computer systemfor processing and yielding an outcome and/or report).

A system typically comprises one or more machines. Each machinecomprises one or more of memory, one or more microprocessors, andinstructions. Where a system includes two or more machines, some or allof the machines may be located at the same location, some or all of themachines may be located at different locations, all of the machines maybe located at one location and/or all of the machines may be located atdifferent locations. Where a system includes two or more machines, someor all of the machines may be located at the same location as a user,some or all of the machines may be located at a location different thana user, all of the machines may be located at the same location as theuser, and/or all of the machine may be located at one or more locationsdifferent than the user.

A system sometimes comprises a computing machine and a sequencingapparatus or machine, where the sequencing apparatus or machine isconfigured to receive physical nucleic acid and generate sequence reads,and the computing apparatus is configured to process the reads from thesequencing apparatus or machine. The computing machine sometimes isconfigured to determine a classification outcome from the sequencereads.

A user may, for example, place a query to software which then mayacquire a data set via internet access, and in certain embodiments, aprogrammable microprocessor may be prompted to acquire a suitable dataset based on given parameters. A programmable microprocessor also mayprompt a user to select one or more data set options selected by themicroprocessor based on given parameters. A programmable microprocessormay prompt a user to select one or more data set options selected by themicroprocessor based on information found via the internet, otherinternal or external information, or the like. Options may be chosen forselecting one or more data feature selections, one or more statisticalalgorithms, one or more statistical analysis algorithms, one or morestatistical significance algorithms, iterative steps, one or morevalidation algorithms, and one or more graphical representations ofmethods, machines, apparatuses, computer programs or a non-transitorycomputer-readable storage medium with an executable program storedthereon.

Systems addressed herein may comprise general components of computersystems, such as, for example, network servers, laptop systems, desktopsystems, handheld systems, personal digital assistants, computingkiosks, and the like. A computer system may comprise one or more inputmeans such as a keyboard, touch screen, mouse, voice recognition orother means to allow the user to enter data into the system. A systemmay further comprise one or more outputs, including, but not limited to,a display screen (e.g., CRT or LCD), speaker, FAX machine, printer(e.g., laser, ink jet, impact, black and white or color printer), orother output useful for providing visual, auditory and/or hardcopyoutput of information (e.g., outcome and/or report).

In a system, input and output components may be connected to a centralprocessing unit which may comprise among other components, amicroprocessor for executing program instructions and memory for storingprogram code and data. In some embodiments, processes may be implementedas a single user system located in a single geographical site. Incertain embodiments, processes may be implemented as a multi-usersystem. In the case of a multi-user implementation, multiple centralprocessing units may be connected by means of a network. The network maybe local, encompassing a single department in one portion of a building,an entire building, span multiple buildings, span a region, span anentire country or be worldwide. The network may be private, being ownedand controlled by a provider, or it may be implemented as an internetbased service where the user accesses a web page to enter and retrieveinformation. Accordingly, in certain embodiments, a system includes oneor more machines, which may be local or remote with respect to a user.More than one machine in one location or multiple locations may beaccessed by a user, and data may be mapped and/or processed in seriesand/or in parallel. Thus, a suitable configuration and control may beutilized for mapping and/or processing data using multiple machines,such as in local network, remote network and/or “cloud” computingplatforms.

A system can include a communications interface in some embodiments. Acommunications interface allows for transfer of software and databetween a computer system and one or more external devices. Non-limitingexamples of communications interfaces include a modem, a networkinterface (such as an Ethernet card), a communications port, a PCMCIAslot and card, and the like. Software and data transferred via acommunications interface generally are in the form of signals, which canbe electronic, electromagnetic, optical and/or other signals capable ofbeing received by a communications interface.

Signals often are provided to a communications interface via a channel Achannel often carries signals and can be implemented using wire orcable, fiber optics, a phone line, a cellular phone link, an RF linkand/or other communications channels. Thus, in an example, acommunications interface may be used to receive signal information thatcan be detected by a signal detection module.

Data may be input by a suitable device and/or method, including, but notlimited to, manual input devices or direct data entry devices (DDEs).Non-limiting examples of manual devices include keyboards, conceptkeyboards, touch sensitive screens, light pens, mouse, tracker balls,joysticks, graphic tablets, scanners, digital cameras, video digitizersand voice recognition devices. Non-limiting examples of DDEs include barcode readers, magnetic strip codes, smart cards, magnetic ink characterrecognition, optical character recognition, optical mark recognition,and turnaround documents.

In some embodiments, output from a sequencing apparatus or machine mayserve as data that can be input via an input device. In certainembodiments, mapped sequence reads may serve as data that can be inputvia an input device. In certain embodiments, nucleic acid template size(e.g., length) may serve as data that can be input via an input device.In certain embodiments, output from a nucleic acid capture process(e.g., genomic region origin data) may serve as data that can be inputvia an input device. In certain embodiments, a combination of nucleicacid template size (e.g., length) and output from a nucleic acid captureprocess (e.g., genomic region origin data) may serve as data that can beinput via an input device. In certain embodiments, simulated data isgenerated by an in silico process and the simulated data serves as datathat can be input via an input device. The term “in silico” refers toresearch and experiments performed using a computer. In silico processesinclude, but are not limited to, mapping sequence reads and processingmapped sequence reads according to processes described herein.

A system may include software useful for performing a process or part ofa process described herein, and software can include one or more modulesfor performing such processes (e.g., sequencing module, logic processingmodule, data display organization module). The term “software” refers tocomputer readable program instructions that, when executed by acomputer, perform computer operations. Instructions executable by theone or more microprocessors sometimes are provided as executable code,that when executed, can cause one or more microprocessors to implement amethod described herein. A module described herein can exist assoftware, and instructions (e.g., processes, routines, subroutines)embodied in the software can be implemented or performed by amicroprocessor. For example, a module (e.g., a software module) can be apart of a program that performs a particular process or task. The term“module” refers to a self-contained functional unit that can be used ina larger machine or software system. A module can comprise a set ofinstructions for carrying out a function of the module. A module cantransform data and/or information. Data and/or information can be in asuitable form. For example, data and/or information can be digital oranalogue. In certain embodiments, data and/or information sometimes canbe packets, bytes, characters, or bits. In some embodiments, data and/orinformation can be any gathered, assembled or usable data orinformation. Non-limiting examples of data and/or information include asuitable media, pictures, video, sound (e.g. frequencies, audible ornon-audible), numbers, constants, a value, objects, time, functions,instructions, maps, references, sequences, reads, mapped reads, levels,ranges, thresholds, signals, displays, representations, ortransformations thereof. A module can accept or receive data and/orinformation, transform the data and/or information into a second form,and provide or transfer the second form to a machine, peripheral,component or another module. A module can perform one or more of thefollowing non-limiting functions: mapping sequence reads, providingcounts, assembling portions, providing or determining a level, providinga count profile, normalizing (e.g., normalizing reads, normalizingcounts, and the like), providing a normalized count profile or levels ofnormalized counts, comparing two or more levels, providing uncertaintyvalues, providing or determining expected levels and expectedranges(e.g., expected level ranges, threshold ranges and thresholdlevels), providing adjustments to levels (e.g., adjusting a first level,adjusting a second level, adjusting a profile of a chromosome or a partthereof, and/or padding), providing identification (e.g., identifying acopy number alteration, genetic variation/genetic alteration oraneuploidy), categorizing, plotting, and/or determining an outcome, forexample. A microprocessor can, in certain embodiments, carry out theinstructions in a module. In some embodiments, one or moremicroprocessors are required to carry out instructions in a module orgroup of modules. A module can provide data and/or information toanother module, machine or source and can receive data and/orinformation from another module, machine or source.

Accordingly, this disclosure also provides systems for determining asequence of nucleotides for one or more nucleic acid templates in anucleic acid sample using the methods as described above. In oneembodiment, the system comprises: one or more processors; and memorycoupled to one or more processors; and the memory is encoded with a setof instructions configured to perform a process comprising: contactingdouble-stranded nucleic acid templates of the nucleic acid sample withpartially double-stranded nonrandom oligonucleotide adapter speciesunder ligation conditions, thereby generating adapter-ligated nucleicacid templates. Each of the nonrandom oligonucleotide adapter speciesmay comprise a first oligonucleotide species and a secondoligonucleotide species; each of the first oligonucleotide speciescomprises 5′ to 3′ a polynucleotide A and a 5′-3′ polynucleotide Bspecies and each of the second oligonucleotide species comprises 5′ to3′ a polynucleotide B′ species and a 5′ to 3′ polynucleotide A′; each ofthe polynucleotide B species and the polynucleotide B′ species arepredetermined, are non-randomly generated, are the same length, and areabout 4 to about 20 consecutive nucleotides in length; there are 300 orfewer polynucleotide B species and each polynucleotide B′ species is areverse complement of a polynucleotide B species; polynucleotide A isnot a reverse complement of polynucleotide A′; the ratio of nucleic acidtemplates to polynucleotide B species is greater than 1,000 to 1; thepolynucleotide B species anneal to the complementary polynucleotide B′species and the polynucleotide A′ species does not anneal to thepolynucleotide A species. The process further comprises amplifying theadapter-ligated nucleic acid templates, thereby generating amplicons andsequencing all or a portion of each amplicon, thereby determining asequence of nucleotides for the one or more nucleic acid templates inthe nucleic acid sample.

A computer program product sometimes is embodied on a tangiblecomputer-readable medium, and sometimes is tangibly embodied on anon-transitory computer-readable medium. A module sometimes is stored ona computer readable medium (e.g., disk, drive) or in memory (e.g.,random access memory). A module and microprocessor capable ofimplementing instructions from a module can be located in a machine orin a different machine. A module and/or microprocessor capable ofimplementing an instruction for a module can be located in the samelocation as a user (e.g., local network) or in a different location froma user (e.g., remote network, cloud system). In embodiments in which amethod is carried out in conjunction with two or more modules, themodules can be located in the same machine, one or more modules can belocated in different machine in the same physical location, and one ormore modules may be located in different machines in different physicallocations.

Accordingly, this disclosure also provides a non-transitory computerreadable storage medium storing instructions that, when executed by oneor more processors of a computing system, cause the computing system toexecute the methods steps disclosed herein. In one embodiment, saidmethod steps comprise contacting double-stranded nucleic acid templatesof the nucleic acid sample with partially double-stranded nonrandomoligonucleotide adapter species under ligation conditions, therebygenerating adapter-ligated nucleic acid templates. Each of the nonrandomoligonucleotide adapter species may comprise a first oligonucleotidespecies and a second oligonucleotide species; each of the firstoligonucleotide species comprises 5′ to 3′ a polynucleotide A and a5′-3′ polynucleotide B species and each of the second oligonucleotidespecies comprises 5′ to 3′ a polynucleotide B′ species and a 5′ to 3′polynucleotide A′; each of the polynucleotide B species and thepolynucleotide B′ species are predetermined, are non-randomly generated,are the same length, and are about 4 to about 20 consecutive nucleotidesin length; there are 300 or fewer polynucleotide B species and eachpolynucleotide B′ species is a reverse complement of a polynucleotide Bspecies; polynucleotide A is not a reverse complement of polynucleotideA′; the ratio of nucleic acid templates to polynucleotide B species isgreater than 1,000 to 1; the polynucleotide B species anneal to thecomplementary polynucleotide B′ species and the polynucleotide A′species does not anneal to the polynucleotide A species. The processfurther comprises amplifying the adapter-ligated nucleic acid templates,thereby generating amplicons and sequencing all or a portion of eachamplicon, thereby determining a sequence of nucleotides for the one ormore nucleic acid templates in the nucleic acid sample.

A machine, in some embodiments, comprises at least one microprocessorfor carrying out the instructions in a module. Sequence readquantifications (e.g., counts) sometimes are accessed by amicroprocessor that executes instructions configured to carry out amethod described herein. Sequence read quantifications that are accessedby a microprocessor can be within memory of a system, and the counts canbe accessed and placed into the memory of the system after they areobtained. In some embodiments, a machine includes a microprocessor(e.g., one or more microprocessors) which microprocessor can performand/or implement one or more instructions (e.g., processes, routinesand/or subroutines) from a module. In some embodiments, a machineincludes multiple microprocessors, such as microprocessors coordinatedand working in parallel. In some embodiments, a machine operates withone or more external microprocessors (e.g., an internal or externalnetwork, server, storage device and/or storage network (e.g., a cloud)).In some embodiments, a machine comprises a module (e.g., one or moremodules). A machine comprising a module often is capable of receivingand transferring one or more of data and/or information to and fromother modules.

In certain embodiments, a machine comprises peripherals and/orcomponents. In certain embodiments, a machine can comprise one or moreperipherals or components that can transfer data and/or information toand from other modules, peripherals and/or components. In certainembodiments, a machine interacts with a peripheral and/or component thatprovides data and/or information. In certain embodiments, peripheralsand components assist a machine in carrying out a function or interactdirectly with a module. Non-limiting examples of peripherals and/orcomponents include a suitable computer peripheral, I/O or storage methodor device including but not limited to scanners, printers, displays(e.g., monitors, LED, LCT or CRTs), cameras, microphones, pads (e.g.,ipads, tablets), touch screens, smart phones, mobile phones, USB I/Odevices, USB mass storage devices, keyboards, a computer mouse, digitalpens, modems, hard drives, jump drives, flash drives, a microprocessor,a server, CDs, DVDs, graphic cards, specialized I/O devices (e.g.,sequencers, photo cells, photo multiplier tubes, optical readers,sensors, etc.), one or more flow cells, fluid handling components,network interface controllers, ROM, RAM, wireless transfer methods anddevices (Bluetooth, WiFi, and the like,), the world wide web (www), theinternet, a computer and/or another module.

Software often is provided on a program product containing programinstructions recorded on a computer readable medium, including, but notlimited to, magnetic media including floppy disks, hard disks, andmagnetic tape; and optical media including CD-ROM discs, DVD discs,magneto-optical discs, flash memory devices (e.g., flash drives), RAM,floppy discs, the like, and other such media on which the programinstructions can be recorded. In online implementation, a server and website maintained by an organization can be configured to provide softwaredownloads to remote users, or remote users may access a remote systemmaintained by an organization to remotely access software. Software mayobtain or receive input information. Software may include a module thatspecifically obtains or receives data (e.g., a data receiving modulethat receives sequence read data and/or mapped read data) and mayinclude a module that specifically processes the data (e.g., aprocessing module that processes received data (e.g., filters,normalizes, provides an outcome and/or report). The terms “obtaining”and “receiving” input information refers to receiving data (e.g.,sequence reads, mapped reads) by computer communication means from alocal, or remote site, human data entry, or any other method ofreceiving data. The input information may be generated in the samelocation at which it is received, or it may be generated in a differentlocation and transmitted to the receiving location. In some embodiments,input information is modified before it is processed (e.g., placed intoa format amenable to processing (e.g., tabulated)).

Software can include one or more algorithms in certain embodiments. Analgorithm may be used for processing data and/or providing an outcome orreport according to a finite sequence of instructions. An algorithmoften is a list of defined instructions for completing a task. Startingfrom an initial state, the instructions may describe a computation thatproceeds through a defined series of successive states, eventuallyterminating in a final ending state. The transition from one state tothe next is not necessarily deterministic (e.g., some algorithmsincorporate randomness). By way of example, and without limitation, analgorithm can be a search algorithm, sorting algorithm, merge algorithm,numerical algorithm, graph algorithm, string algorithm, modelingalgorithm, computational genometric algorithm, combinatorial algorithm,machine learning algorithm, cryptography algorithm, data compressionalgorithm, parsing algorithm and the like. An algorithm can include onealgorithm or two or more algorithms working in combination. An algorithmcan be of any suitable complexity class and/or parameterized complexity.An algorithm can be used for calculation and/or data processing, and insome embodiments, can be used in a deterministic orprobabilistic/predictive approach. An algorithm can be implemented in acomputing environment by use of a suitable programming language,non-limiting examples of which are C, C++, Java, Perl, Python, Fortran,and the like. In some embodiments, an algorithm can be configured ormodified to include margin of errors, statistical analysis, statisticalsignificance, and/or comparison to other information or data sets (e.g.,applicable when using a neural net or clustering algorithm).

In certain embodiments, several algorithms may be implemented for use insoftware. These algorithms can be trained with raw data in someembodiments. For each new raw data sample, the trained algorithms mayproduce a representative processed data set or outcome. A processed dataset sometimes is of reduced complexity compared to the parent data setthat was processed. Based on a processed set, the performance of atrained algorithm may be assessed based on sensitivity and specificity,in some embodiments. An algorithm with the highest sensitivity and/orspecificity may be identified and utilized, in certain embodiments.

In certain embodiments, simulated (or simulation) data can aid dataprocessing, for example, by training an algorithm or testing analgorithm. In some embodiments, simulated data includes hypotheticalvarious samplings of different groupings of sequence reads. Simulateddata may be based on what might be expected from a real population ormay be skewed to test an algorithm and/or to assign a correctclassification. Simulated data also is referred to herein as “virtual”data. Simulations can be performed by a computer program in certainembodiments. One possible step in using a simulated data set is toevaluate the confidence of identified results, e.g., how well a randomsampling matches or best represents the original data. One approach isto calculate a probability value (p-value), which estimates theprobability of a random sample having better score than the selectedsamples. In some embodiments, an empirical model may be assessed, inwhich it is assumed that at least one sample matches a reference sample(with or without resolved variations). In some embodiments, anotherdistribution, such as a Poisson distribution for example, can be used todefine the probability distribution.

A system may include one or more microprocessors in certain embodiments.A microprocessor can be connected to a communication bus. A computersystem may include a main memory, often random access memory (RAM), andcan also include a secondary memory. Memory in some embodimentscomprises a non-transitory computer-readable storage medium. Secondarymemory can include, for example, a hard disk drive and/or a removablestorage drive, representing a floppy disk drive, a magnetic tape drive,an optical disk drive, memory card and the like. A removable storagedrive often reads from and/or writes to a removable storage unit.Non-limiting examples of removable storage units include a floppy disk,magnetic tape, optical disk, and the like, which can be read by andwritten to by, for example, a removable storage drive. A removablestorage unit can include a computer-usable storage medium having storedtherein computer software and/or data.

A microprocessor may implement software in a system. In someembodiments, a microprocessor may be programmed to automatically performa task described herein that a user could perform. Accordingly, amicroprocessor, or algorithm conducted by such a microprocessor, canrequire little to no supervision or input from a user (e.g., softwaremay be programmed to implement a function automatically). In someembodiments, the complexity of a process is so large that a singleperson or group of persons could not perform the process in a timeframeshort enough for determining the presence or absence of a geneticvariation or genetic alteration.

In some embodiments, secondary memory may include other similar meansfor allowing computer programs or other instructions to be loaded into acomputer system. For example, a system can include a removable storageunit and an interface device. Non-limiting examples of such systemsinclude a program cartridge and cartridge interface (such as that foundin video game devices), a removable memory chip (such as an EPROM, orPROM) and associated socket, and other removable storage units andinterfaces that allow software and data to be transferred from theremovable storage unit to a computer system.

FIG. 1 illustrates a non-limiting example of a computing environment 110in which various systems, methods, algorithms, and data structuresdescribed herein may be implemented. The computing environment 110 isonly one example of a suitable computing environment and is not intendedto suggest any limitation as to the scope of use or functionality of thesystems, methods, and data structures described herein. Neither shouldcomputing environment 110 be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin computing environment 110. A subset of systems, methods, and datastructures shown in FIG. 1 can be utilized in certain embodiments.Systems, methods, and data structures described herein are operationalwith numerous other general purpose or special purpose computing systemenvironments or configurations. Examples of known computing systems,environments, and/or configurations that may be suitable include, butare not limited to, personal computers, server computers, thin clients,thick clients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

The operating environment 110 of FIG. 1 includes a general purposecomputing device in the form of a computer 120, including a processingunit 121, a system memory 122, and a system bus 123 that operativelycouples various system components including the system memory 122 to theprocessing unit 121. There may be only one or there may be more than oneprocessing unit 121, such that the processor of computer 120 includes asingle central-processing unit (CPU), or a plurality of processingunits, commonly referred to as a parallel processing environment. Thecomputer 120 may be a conventional computer, a distributed computer, orany other type of computer.

The system bus 123 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memorymay also be referred to as simply the memory, and includes read onlymemory (ROM) 124 and random access memory (RAM). A basic input/outputsystem (BIOS) 126, containing the basic routines that help to transferinformation between elements within the computer 120, such as duringstart-up, is stored in ROM 124. The computer 120 may further include ahard disk drive interface 127 for reading from and writing to a harddisk, not shown, a magnetic disk drive 128 for reading from or writingto a removable magnetic disk 129, and an optical disk drive 130 forreading from or writing to a removable optical disk 131 such as a CD ROMor other optical media.

The hard disk drive 127, magnetic disk drive 128, and optical disk drive130 are connected to the system bus 123 by a hard disk drive interface132, a magnetic disk drive interface 133, and an optical disk driveinterface 134, respectively. The drives and their associatedcomputer-readable media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputer 120. Any type of computer-readable media that can store datathat is accessible by a computer, such as magnetic cassettes, flashmemory cards, digital video disks, Bernoulli cartridges, random accessmemories (RAMs), read only memories (ROMs), and the like, may be used inthe operating environment.

A number of program modules may be stored on the hard disk, magneticdisk 129, optical disk 131, ROM 124, or RAM, including an operatingsystem 135, one or more application programs 136, other program modules137, and program data 138. A user may enter commands and informationinto the personal computer 120 through input devices such as a keyboard140 and pointing device 142. Other input devices (not shown) may includea microphone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit121 through a serial port interface 146 that is coupled to the systembus, but may be connected by other interfaces, such as a parallel port,game port, or a universal serial bus (USB). A monitor 147 or other typeof display device is also connected to the system bus 123 via aninterface, such as a video adapter 148. In addition to the monitor,computers typically include other peripheral output devices (not shown),such as speakers and printers.

The computer 120 may operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer149. These logical connections may be achieved by a communication devicecoupled to or a part of the computer 120, or in other manners. Theremote computer 149 may be another computer, a server, a router, anetwork PC, a client, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 120, although only a memory storage device 150 has beenillustrated in FIG. 1 . The logical connections depicted in FIG. 1include a local-area network (LAN) 151 and a wide-area network (WAN)152. Such networking environments are commonplace in office networks,enterprise-wide computer networks, intranets and the Internet, which allare types of networks.

When used in a LAN-networking environment, the computer 120 is connectedto the local network 151 through a network interface or adapter 153,which is one type of communications device. When used in aWAN-networking environment, the computer 120 often includes a modem 154,a type of communications device, or any other type of communicationsdevice for establishing communications over the wide area network 152.The modem 154, which may be internal or external, is connected to thesystem bus 123 via the serial port interface 146. In a networkedenvironment, program modules depicted relative to the personal computer120, or portions thereof, may be stored in the remote memory storagedevice. It is appreciated that the network connections shown arenon-limiting examples and other communications devices for establishinga communications link between computers may be used.

Transformations

As noted above, data sometimes is transformed from one form into anotherform. The terms “transformed,” “transformation,” and grammaticalderivations or equivalents thereof, as used herein refer to analteration of data from a physical starting material (e.g., test subjectand/or reference subject sample nucleic acid) into a digitalrepresentation of the physical starting material (e.g., sequence readdata), and in some embodiments includes a further transformation intoone or more numerical values or graphical representations of the digitalrepresentation that can be utilized to provide an outcome. In certainembodiments, the one or more numerical values and/or graphicalrepresentations of digitally represented data can be utilized torepresent the appearance of a test subject's physical genome (e.g.,virtually represent or visually represent the presence or absence of agenomic insertion, duplication or deletion; represent the presence orabsence of a variation in the physical amount of a sequence associatedwith medical conditions). A virtual representation sometimes is furthertransformed into one or more numerical values or graphicalrepresentations of the digital representation of the starting material.These methods can transform physical starting material into a numericalvalue or graphical representation, or a representation of the physicalappearance of a test subject's nucleic acid.

In some embodiments, transformation of a data set facilitates providingan outcome by reducing data complexity and/or data dimensionality. Dataset complexity sometimes is reduced during the process of transforming aphysical starting material into a virtual representation of the startingmaterial (e.g., sequence reads representative of physical startingmaterial). A suitable feature or variable can be utilized to reduce dataset complexity and/or dimensionality. Non-limiting examples of featuresthat can be chosen for use as a target feature for data processinginclude GC content, fetal gender prediction, fragment size (e.g., lengthof CCF fragments, reads or a suitable representation thereof (e.g.,FRS)), fragment sequence, identification of a copy number alteration,identification of chromosomal aneuploidy, identification of particulargenes or proteins, identification of cancer, diseases, inheritedgenes/traits, chromosomal abnormalities, a biological category, achemical category, a biochemical category, a category of genes orproteins, a gene ontology, a protein ontology, co-regulated genes, cellsignaling genes, cell cycle genes, proteins pertaining to the foregoinggenes, gene variants, protein variants, co-regulated genes, co-regulatedproteins, amino acid sequence, nucleotide sequence, protein structuredata and the like, and combinations of the foregoing. Non-limitingexamples of data set complexity and/or dimensionality reduction include;reduction of a plurality of sequence reads to profile plots, reductionof a plurality of sequence reads to numerical values (e.g., normalizedvalues, Z-scores, p-values); reduction of multiple analysis methods toprobability plots or single points; principal component analysis ofderived quantities; and the like or combinations thereof.

Genetic Variations/Genetic Alterations and Medical Conditions

The presence or absence of a genetic variation can be determined using amethod or apparatus described herein. A genetic variation also may bereferred to as a genetic alteration, and the terms are often usedinterchangeably herein and in the art. In certain instances, “geneticalteration” may be used to describe a somatic alteration whereby thegenome in a subset of cells in a subject contains the alteration (suchas, for example, in tumor or cancer cells). In certain instances,“genetic variation” may be used to describe a variation inherited fromone or both parents (such as, for example, a genetic variation in afetus).

In certain embodiments, the presence or absence of one or more geneticvariations or genetic alterations is determined according to an outcomeprovided by methods and apparatuses described herein. A geneticvariation generally is a particular genetic phenotype present in certainindividuals, and often a genetic variation is present in a statisticallysignificant sub-population of individuals. In some embodiments, agenetic variation or genetic alteration is a chromosome abnormality orcopy number alteration (e.g., aneuploidy, duplication of one or morechromosomes, loss of one or more chromosomes, partial chromosomeabnormality or mosaicism (e.g., loss or gain of one or more regions of achromosome), translocation, inversion, each of which is described ingreater detail herein). Non-limiting examples of geneticvariations/genetic alterations include one or more copy numberalterations/variations, deletions (e.g., microdeletions), duplications(e.g., microduplications), insertions, mutations (e.g., singlenucleotide variations, single nucleotide alterations), polymorphisms(e.g., single-nucleotide polymorphisms), fusions, repeats (e.g., shorttandem repeats), distinct methylation sites, distinct methylationpatterns, the like and combinations thereof. An insertion, repeat,deletion, duplication, mutation or polymorphism can be of any length,and in some embodiments, is about 1 base or base pair (bp) to about 250megabases (Mb) in length. In some embodiments, an insertion, repeat,deletion, duplication, mutation or polymorphism is about 1 base or basepair (bp) to about 50,000 kilobases (kb) in length (e.g., about 10 bp,50 bp, 100 bp, 500 bp, 1 kb, 5 kb, 10kb, 50 kb, 100 kb, 500 kb, 1000 kb,5000 kb or 10,000 kb in length).

A genetic variation or genetic alteration is sometime a deletion. Incertain instances, a deletion is a mutation (e.g., a genetic aberration)in which a part of a chromosome or a sequence of DNA is missing. Adeletion is often the loss of genetic material. Any number ofnucleotides can be deleted. A deletion can comprise the deletion of oneor more entire chromosomes, a region of a chromosome, an allele, a gene,an intron, an exon, any non-coding region, any coding region, a partthereof or combination thereof. A deletion can comprise a microdeletion.A deletion can comprise the deletion of a single base.

A genetic variation or genetic alteration is sometimes a duplication. Incertain instances, a duplication is a mutation (e.g., a geneticaberration) in which a part of a chromosome or a sequence of DNA iscopied and inserted back into the genome. In certain embodiments, agenetic duplication (e.g., duplication) is any duplication of a regionof DNA. In some embodiments, a duplication is a nucleic acid sequencethat is repeated, often in tandem, within a genome or chromosome. Insome embodiments, a duplication can comprise a copy of one or moreentire chromosomes, a region of a chromosome, an allele, a gene, anintron, an exon, any non-coding region, any coding region, part thereofor combination thereof. A duplication can comprise a microduplication. Aduplication sometimes comprises one or more copies of a duplicatednucleic acid. A duplication sometimes is characterized as a geneticregion repeated one or more times (e.g., repeated 1, 2, 3, 4, 5, 6, 7,8, 9 or 10 times). Duplications can range from small regions (thousandsof base pairs) to whole chromosomes in some instances. Duplicationsfrequently occur as the result of an error in homologous recombinationor due to a retrotransposon event. Duplications have been associatedwith certain types of proliferative diseases. Duplications can becharacterized using genomic microarrays or comparative genetichybridization (CGH).

A genetic variation or genetic alteration is sometimes an insertion. Aninsertion is sometimes the addition of one or more nucleotide base pairsinto a nucleic acid sequence. An insertion is sometimes amicroinsertion. In certain embodiments, an insertion comprises theaddition of a region of a chromosome into a genome, chromosome, or partthereof In certain embodiments, an insertion comprises the addition ofan allele, a gene, an intron, an exon, any non-coding region, any codingregion, part thereof or combination thereof into a genome or partthereof. In certain embodiments, an insertion comprises the addition(e.g., insertion) of nucleic acid of unknown origin into a genome,chromosome, or part thereof. In certain embodiments, an insertioncomprises the addition (e.g., insertion) of a single base.

As used herein a “copy number alteration” generally is a class or typeof genetic variation, genetic alteration or chromosomal aberration. Acopy number alteration also may be referred to as a copy numbervariation, and the terms are often used interchangeably herein and inthe art. In certain instances, “copy number alteration” may be used todescribe a somatic alteration whereby the genome in a subset of cells ina subject contains the alteration (such as, for example, in tumor orcancer cells). In certain instances, “copy number variation” may be usedto describe a variation inherited from one or both parents (such as, forexample, a copy number variation in a fetus). A copy number alterationcan be a deletion (e.g., microdeletion), duplication (e.g., amicroduplication) or insertion (e.g., a microinsertion). Often, theprefix “micro” as used herein sometimes is a region of nucleic acid lessthan 5 Mb in length. A copy number alteration can include one or moredeletions (e.g., microdeletion), duplications and/or insertions (e.g., amicroduplication, microinsertion) of a part of a chromosome. In certainembodiments, a duplication comprises an insertion. In certainembodiments, an insertion is a duplication. In certain embodiments, aninsertion is not a duplication.

In some embodiments, a copy number alteration is a copy numberalteration from a tumor or cancer cell. In some embodiments, a copynumber alteration is a copy number alteration from a non-cancer cell. Incertain embodiments, a copy number alteration is a copy numberalteration within the genome of a subject (e.g., a cancer patient)and/or within the genome of a cancer cell or tumor in a subject. A copynumber alteration can be a heterozygous copy number alteration where thevariation (e.g., a duplication or deletion) is present on one allele ofa genome. A copy number alteration can be a homozygous copy numberalteration where the alteration is present on both alleles of a genome.In some embodiments, a copy number alteration is a heterozygous orhomozygous copy number alteration. In some embodiments, a copy numberalteration is a heterozygous or homozygous copy number alteration from acancer cell or non-cancer cell. A copy number alteration sometimes ispresent in a cancer cell genome and a non-cancer cell genome, a cancercell genome and not a non-cancer cell genome, or a non-cancer cellgenome and not a cancer cell genome.

In some embodiments, a copy number alteration is a fetal copy numberalteration. Often, a fetal copy number alteration is a copy numberalteration in the genome of a fetus. In some embodiments, a copy numberalteration is a maternal and/or fetal copy number alteration. In certainembodiments, a maternal and/or fetal copy number alteration is a copynumber alteration within the genome of a pregnant female (e.g., a femalesubject bearing a fetus), a female subject that gave birth or a femalecapable of bearing a fetus. A copy number alteration can be aheterozygous copy number alteration where the alteration (e.g., aduplication or deletion) is present on one allele of a genome. A copynumber alteration can be a homozygous copy number alteration where thealteration is present on both alleles of a genome. In some embodiments,a copy number alteration is a heterozygous or homozygous fetal copynumber alteration. In some embodiments, a copy number alteration is aheterozygous or homozygous maternal and/or fetal copy number alteration.A copy number alteration sometimes is present in a maternal genome and afetal genome, a maternal genome and not a fetal genome, or a fetalgenome and not a maternal genome.

“Ploidy” is a reference to the number of chromosomes present in asubject. In certain embodiments, “ploidy” is the same as “chromosomeploidy.” In humans, for example, autosomal chromosomes are often presentin pairs. For example, in the absence of a genetic variation or geneticalteration, most humans have two of each autosomal chromosome (e.g.,chromosomes 1-22). The presence of the normal complement of 2 autosomalchromosomes in a human is often referred to as euploid or diploid.“Microploidy” is similar in meaning to ploidy. “Microploidy” oftenrefers to the ploidy of a part of a chromosome. The term “microploidy”sometimes is a reference to the presence or absence of a copy numberalteration (e.g., a deletion, duplication and/or an insertion) within achromosome (e.g., a homozygous or heterozygous deletion, duplication, orinsertion, the like or absence thereof).

A genetic variation or genetic alteration for which the presence orabsence is identified for a subject is associated with a medicalcondition in certain embodiments. Thus, technology described herein canbe used to identify the presence or absence of one or more geneticvariations or genetic alterations that are associated with a medicalcondition or medical state. Non-limiting examples of medical conditionsinclude those associated with intellectual disability (e.g., DownSyndrome), aberrant cell-proliferation (e.g., cancer), presence of amicro-organism nucleic acid (e.g., virus, bacterium, fungus, yeast), andpreeclampsia.

Non-limiting examples of genetic variations/genetic alterations, medicalconditions and states are described hereafter.

Chromosome Abnormalities

In some embodiments, the presence or absence of a chromosome abnormalitycan be determined by using a method and/or apparatus described herein.Chromosome abnormalities include, without limitation, copy numberalterations, and a gain or loss of an entire chromosome or a region of achromosome comprising one or more genes. Chromosome abnormalitiesinclude monosomies, trisomies, polysomies, loss of heterozygosity,translocations, deletions and/or duplications of one or more nucleotidesequences (e.g., one or more genes), including deletions andduplications caused by unbalanced translocations. The term “chromosomalabnormality” or “aneuploidy” as used herein refer to a deviation betweenthe structure of the subject chromosome and a normal homologouschromosome. The term “normal” refers to the predominate karyotype orbanding pattern found in healthy individuals of a particular species,for example, a euploid genome (e.g., diploid in humans, e.g., 46,XX or46,XY). As different organisms have widely varying chromosomecomplements, the term “aneuploidy” does not refer to a particular numberof chromosomes, but rather to the situation in which the chromosomecontent within a given cell or cells of an organism is abnormal. In someembodiments, the term “aneuploidy” herein refers to an imbalance ofgenetic material caused by a loss or gain of a whole chromosome, or partof a chromosome. An “aneuploidy” can refer to one or more deletionsand/or insertions of a region of a chromosome. The term “euploid,” insome embodiments, refers a normal complement of chromosomes.

The term “monosomy” as used herein refers to lack of one chromosome ofthe normal complement. Partial monosomy can occur in unbalancedtranslocations or deletions, in which only a part of the chromosome ispresent in a single copy. Monosomy of sex chromosomes (45, X) causesTurner syndrome, for example. The term “disomy” refers to the presenceof two copies of a chromosome. For organisms such as humans that havetwo copies of each chromosome (those that are diploid or “euploid”),disomy is the normal condition. For organisms that normally have threeor more copies of each chromosome (those that are triploid or above),disomy is an aneuploid chromosome state. In uniparental disomy, bothcopies of a chromosome come from the same parent (with no contributionfrom the other parent).

The term “trisomy” as used herein refers to the presence of threecopies, instead of two copies, of a particular chromosome. The presenceof an extra chromosome 21, which is found in human Down syndrome, isreferred to as “Trisomy 21.” Trisomy 18 and Trisomy 13 are two otherhuman autosomal trisomies. Trisomy of sex chromosomes can be seen infemales (e.g., 47, XXX in Triple X Syndrome) or males (e.g., 47, XXY inKlinefelter's Syndrome; or 47,XYY in Jacobs Syndrome). In someembodiments, a trisomy is a duplication of most or all of an autosome.In certain embodiments, a trisomy is a whole chromosome aneuploidyresulting in three instances (e.g., three copies) of a particular typeof chromosome (e.g., instead of two instances (e.g., a pair) of aparticular type of chromosome for a euploid).

The terms “tetrasomy” and “pentasomy” as used herein refer to thepresence of four or five copies of a chromosome, respectively. Althoughrarely seen with autosomes, sex chromosome tetrasomy and pentasomy havebeen reported in humans, including XXXX, XXXY, XXYY, XYYY, XXXXX, XXXXY,XXXYY, XXYYY and XYYYY.

Medical Disorders and Medical Conditions

Methods described herein can be applicable to any suitable medicaldisorder or medical condition. Non-limiting examples of medicaldisorders and medical conditions include cell proliferative disordersand conditions, wasting disorders and conditions, degenerative disordersand conditions, autoimmune disorders and conditions, pre-eclampsia,chemical or environmental toxicity, liver damage or disease, kidneydamage or disease, vascular disease, high blood pressure, and myocardialinfarction.

In some embodiments, a cell proliferative disorder or conditionsometimes is a cancer, tumor, neoplasm, metastatic disease, the like orcombination thereof. A cell proliferative disorder or conditionsometimes is a disorder or condition of the liver, lung, spleen,pancreas, colon, skin, bladder, eye, brain, esophagus, head, neck,ovary, testes, prostate, the like or combination thereof Non-limitingexamples of cancers include hematopoietic neoplastic disorders, whichare diseases involving hyperplastic/neoplastic cells of hematopoieticorigin (e.g., arising from myeloid, lymphoid or erythroid lineages, orprecursor cells thereof), and can arise from poorly differentiated acuteleukemias (e.g., erythroblastic leukemia and acute megakaryoblasticleukemia). Certain myeloid disorders include, but are not limited to,acute promyeloid leukemia (APML), acute myelogenous leukemia (AML) andchronic myelogenous leukemia (CML). Certain lymphoid malignanciesinclude, but are not limited to, acute lymphoblastic leukemia (ALL),which includes B-lineage ALL and T-lineage ALL, chronic lymphocyticleukemia (CLL), prolymphocytic leukemia (PLL), hairy cell leukemia (HLL)and Waldenstrom's macroglobulinemia (WM). Certain forms of malignantlymphomas include, but are not limited to, non-Hodgkin lymphoma andvariants thereof, peripheral T cell lymphomas, adult T cellleukemia/lymphoma (ATL), cutaneous T-cell lymphoma (CTCL), largegranular lymphocytic leukemia (LGF), Hodgkin's disease andReed-Sternberg disease. A cell proliferative disorder sometimes is anon-endocrine tumor or endocrine tumor. Illustrative examples ofnon-endocrine tumors include, but are not limited to, adenocarcinomas,acinar cell carcinomas, adenosquamous carcinomas, giant cell tumors,intraductal papillary mucinous neoplasms, mucinous cystadenocarcinomas,pancreatoblastomas, serous cystadenomas, solid and pseudopapillarytumors. An endocrine tumor sometimes is an islet cell tumor.

In some embodiments, a wasting disorder or condition, or degenerativedisorder or condition, is cirrhosis, amyotrophic lateral sclerosis(ALS), Alzheimer's disease, Parkinson's disease, multiple systematrophy, atherosclerosis, progressive supranuclear palsy, Tay-Sachsdisease, diabetes, heart disease, keratoconus, inflammatory boweldisease (IBD), prostatitis, osteoarthritis, osteoporosis, rheumatoidarthritis, Huntington's disease, chronic traumatic encephalopathy,chronic obstructive pulmonary disease (COPD), tuberculosis, chronicdiarrhea, acquired immune deficiency syndrome (AIDS), superiormesenteric artery syndrome, the like or combination thereof.

In some embodiments, an autoimmune disorder or condition is acutedisseminated encephalomyelitis (ADEM), Addison's disease, alopeciaareata, ankylosing spondylitis, antiphospholipid antibody syndrome(APS), autoimmune hemolytic anemia, autoimmune hepatitis, autoimmuneinner ear disease, bullous pemphigoid, celiac disease, Chagas disease,chronic obstructive pulmonary disease, Crohns Disease (a type ofidiopathic inflammatory bowel disease “IBD”), dermatomyositis, diabetesmellitus type 1, endometriosis, Goodpasture's syndrome, Graves' disease,Guillain-Barré syndrome (GBS), Hashimoto's disease, hidradenitissuppurativa, idiopathic thrombocytopenic purpura, interstitial cystitis,Lupus erythematosus, mixed connective tissue disease, morphea, multiplesclerosis (MS), myasthenia gravis, narcolepsy, euromyotonia, pemphigusvulgaris, pernicious anaemia, polymyositis, primary biliary cirrhosis,rheumatoid arthritis, schizophrenia, scleroderma, Sjögren's syndrome,temporal arteritis (also known as “giant cell arteritis”), ulcerativecolitis (a type of idiopathic inflammatory bowel disease “IBD”),vasculitis, vitiligo, Wegener's granulomatosis, the like or combinationthereof.

Preeclampsia

In some embodiments, the presence or absence of preeclampsia isdetermined by using a method or apparatus described herein. Preeclampsiais a condition in which hypertension arises in pregnancy (e.g.,pregnancy-induced hypertension) and is associated with significantamounts of protein in the urine. In certain instances, preeclampsia maybe associated with elevated levels of extracellular nucleic acid and/oralterations in methylation patterns. For example, a positive correlationbetween extracellular fetal-derived hypermethylated RASSF1A levels andthe severity of pre-eclampsia has been observed. In certain instances,increased DNA methylation is observed for the H19 gene in preeclampticplacentas compared to normal controls.

Pathogens

In some embodiments, the presence or absence of a pathogenic conditionis determined by a method or apparatus described herein. A pathogeniccondition can be caused by infection of a host by a pathogen including,but not limited to, a bacterium, virus or fungus. Since pathogenstypically possess nucleic acid (e.g., genomic DNA, genomic RNA, mRNA)that can be distinguishable from host nucleic acid, methods, machinesand apparatus provided herein can be used to determine the presence orabsence of a pathogen. Often, pathogens possess nucleic acid withcharacteristics unique to a particular pathogen such as, for example,epigenetic state and/or one or more sequence variations, duplicationsand/or deletions. Thus, methods provided herein may be used to identifya particular pathogen or pathogen variant (e.g., strain).

Use of Cell Free Nucleic Acid

In certain instances, nucleic acid from abnormal or diseased cellsassociated with a particular condition or disorder is released from thecells as circulating cell-free nucleic acid (CCF-NA). For example,cancer cell nucleic acid is present in CCF-NA, and analysis of CCF-NAusing methods provided herein can be used to determining whether asubject has, or is at risk of having, cancer. Analysis of the presenceor absence of cancer cell nucleic acid in CCF-NA can be used for cancerscreening, for example. In certain instances, levels of CCF-NA in serumcan be elevated in patients with various types of cancer compared withhealthy patients. Patients with metastatic diseases, for example, cansometimes have serum DNA levels approximately twice as high asnon-metastatic patients. Accordingly, methods described herein canprovide an outcome by processing sequencing read counts obtained fromCCF-NA extracted from a sample from a subject (e.g., a subject having,suspected of having, predisposed to, or suspected as being predisposedto, a particular condition or disease).

Markers

In certain instances, a polynucleotide in abnormal or diseased cells ismodified with respect to nucleic acid in normal or non-diseased cells(e.g., single nucleotide alteration, single nucleotide variation, copynumber alteration, copy number variation). In some instances, apolynucleotide is present in abnormal or diseased cells and not presentin normal or non-diseased cells, and sometimes a polynucleotide is notpresent in abnormal or diseased cells and is present in normal ornon-diseased cells. Thus, a marker sometimes is a single nucleotidealteration/variation and/or a copy number alteration/variation (e.g., adifferentially expressed DNA or RNA (e.g., mRNA)). For example, patientswith metastatic diseases may be identified by cancer-specific markersand/or certain single nucleotide polymorphisms or short tandem repeats,for example. Non-limiting examples of cancer types that may bepositively correlated with elevated levels of circulating DNA includebreast cancer, colorectal cancer, gastrointestinal cancer,hepatocellular cancer, lung cancer, melanoma, non-Hodgkin lymphoma,leukemia, multiple myeloma, bladder cancer, hepatoma, cervical cancer,esophageal cancer, pancreatic cancer, and prostate cancer. Variouscancers can possess, and can sometimes release into the bloodstream,nucleic acids with characteristics that are distinguishable from nucleicacids from non-cancerous healthy cells, such as, for example, epigeneticstate and/or sequence variations, duplications and/or deletions. Suchcharacteristics can, for example, be specific to a particular type ofcancer. Accordingly, methods described herein sometimes provide anoutcome based on determining the presence or absence of a particularmarker, and sometimes an outcome is presence or absence of a particulartype of condition (e.g., a particular type of cancer).

Certain methods described herein may be performed in conjunction withmethods described, for example in International Patent ApplicationPublication No. WO2013/052913, International Patent ApplicationPublication No. WO2013/052907, International Patent ApplicationPublication No. WO2013/055817, International Patent ApplicationPublication No. WO2013/109981, International Patent ApplicationPublication No. WO2013/177086, International Patent ApplicationPublication No. WO2013/192562, International Patent ApplicationPublication No. WO2014/116598, International Patent ApplicationPublication No. WO2014/055774, International Patent ApplicationPublication No. WO2014/190286, International Patent ApplicationPublication No. WO2014/205401, International Patent ApplicationPublication No. WO2015/051163, International Patent ApplicationPublication No. WO2015/138774, International Patent ApplicationPublication No. WO2015/054080, International Patent ApplicationPublication No. WO2015/183872, International Patent ApplicationPublication No. WO2016/019042, and International Patent ApplicationPublication No. WO 2016/057901, the entire content of each isincorporated herein by reference, including all text, tables, equationsand drawings.

EXAMPLES

The examples set forth below illustrate certain embodiments and do notlimit the technology.

Example 1 Materials and Methods

The materials and methods set forth in this example were used, or can beused, to perform certain aspects of the methods and analysis describedin Examples 2 and 3, except where otherwise noted.

DNA Extraction

Whole blood is collected, for example in Streck BCT tubes, and processedto plasma using the methods previously described (see e.g., Jensen etal. (2013) PLoS One 8(3): e57381). DNA extraction from plasma isperformed using Hamilton liquid handlers.

Library Preparation

After extraction, ccf DNA is used to create sequencing libraries. Thisprocess includes the following enzymatic reactions: end repair,mono-adenylation (a-tailing), adapter ligation, and PCR. Adaptersinclude single molecule barcodes or unique molecule identifiers. Sincethe ligation process occurs prior to PCR, single molecule barcodesenable the differentiation of unique template molecules and can beuseful for error correction.

In one example, indexed and single molecule barcoded sequencinglibraries were prepared from plasma DNA samples for sequencing onIllumina instruments using NEBNEXT ULTRA biochemistry modified forOncology library custom adapters. Specifically, custom single moleculebarcoded library adapters were hybridized in plate format prior topreparation of sequencing libraries. Generation of Y-shaped customsingle molecule barcoded library adapters was achieved by mixing customP5 and P7 oligonucleotides in equimolar concentration in STE buffer,denaturing this mixture on a thermal cycler, and slowly ramping to roomtemperature. Sequencing libraries were prepared in a multi-step,automated process using the ZEPHYR liquid handler. The starting materialwas 40 μL of DNA extracted from plasma or 40 μL of fragmented and sizeselected DNA extracted from tissue or buffy coat samples. During NEBNEXTULTRA/Oncology library preparation, a series of enzymatic reactions wereperformed to modify the dsDNA fragments such that the molecules wereamenable to clustering and sequencing on Illumina sequencing platforms.These included: 1) end preparation, 2) adapter ligation, and 3) PCR.Adapter ligation and PCR were each followed by a cleanup step usingAMPURE XP beads to remove excess proteins and nucleotides prior tofurther downstream processing. These cleanup steps were automated in a96-well plate format. In the first enzymatic step, combined End PrepEnzyme Mix (NEB) and 10× End Repair Reaction Buffer (NEB) were usedto: 1) create blunt-ended, 5′ phosphorylated fragments via exonucleaseand polymerase activities and 2) add a single adenine nucleotide to 3′fragment ends (A-tailing) in order to minimize the incidence of templateconcatenation and facilitate adapter ligation. To achieve these combinedactivities, a brief heat inactivation step immediately after blunt-endformation substituted for a more traditional magnetic bead cleanup andcatalyzed the A-tailing of DNA fragments. These 3′ adenine overhangswere complementary to the thymine overhangs present on custom singlemolecule barcoded library adapters. The addition of double-strandedY-shaped adapters to the A-tailed fragments was mediated by Blunt/TALigase Master Mix (NEB) and Ligation Enhancer (NEB) in the secondenzymatic reaction. Finally, DNA fragments with adapters properlyligated at both ends were selectively amplified using universal forwardand universal reverse PCR primers and NEBNEXT Hot Start High-Fidelity 2×PCR Master Mix. The library preparation process was performed in two labspaces separated by traditional pre- and post-PCR restrictions. Thefinal PCR cleanup step yielded stock libraries eluted in HPLC water thatwere suitable for dilution, QC, normalization, target captureenrichment, and sequencing on Illumina instruments.

Quantification

After library preparation, the libraries are quantified using capillaryelectrophoresis (e.g., CaliperGX) or PCR-based methods (e.g., dropletdigital PCR, quantitative PCR). A fixed amount of library is then usedas the template for target enrichment. The amount of library used fortarget enrichment is dependent upon the number of samples multiplexedtogether prior to target enrichment (e.g., 1 to 24 samples).

Target Enrichment

In order to enable for a certain level of sequencing (e.g., 30,000-foldto 50,000-fold) using as few sequencing reads as possible, hybridizationcapture methods are utilized to enrich for genomic regions of interest.For this process, biotinylated probes (sometimes referred to as baits)are designed to span regions of interest, manufactured, and pooledtogether in a single reaction well. The target enrichment process worksby first denaturing the library/libraries and then hybridizingbiotinylated probes to the target libraries. This process occurs at anelevated temperature (45° C. to 65° C.) for an extended period of time(4 to 72 hours). Upon completion of the hybridization process, thehybridized probe/library complex is then precipitated using streptavidincoated beads. The beads are washed and the enriched libraries are thenamplified using an additional PCR reaction, similar to the PCR reactionused during library preparation. Target enrichment probes may becommercially manufactured, for example by integrated DNA technologies(IDT) and/or Roche/Nimblegen, and may be about 60 to 120 bp in length.

In one example, single molecular barcode indexed libraries were targetcaptured using an oncology probe panel for sequencing on Illuminainstruments (certain genes represented in the oncology probe panel aredescribed in Example 4 and presented in Table 2). Specifically, singlemolecular indexed libraries were captured in a multi-step manualprocess. The starting material was an adapter ligated, cleaned libraryeluted in 50 μL of water prepared on the ZEPHYR liquid handler usingNEBNEXT Ultra Biochemistry. During the target capture procedure, aseries of steps were performed to capture desired target loci which wereamenable to clustering and sequencing on Illumina's HISEQ 2500instruments including 1) blocking of repetitive elements and adaptersequences, 2) hybridization of capture probes to target DNA, 3) beadbinding of capture probes and washing, 3) PCR amplification, and 4) beadcleanup. In the first step, blocking oligos complementary to theIllumina adapters were added along with Cot-1 DNA that blockedrepetitive elements in the genome. The blocked DNA was then dried in aCENTRIVAP concentrator centrifuge at 65° C. until samples werecompletely evaporated. The dried samples were then immediatelyresuspended using hybridization buffer. During hybridization, thetemplates were denatured and the blocking elements and biotinylatedcapture probes subsequently were hybridized. The bound templates werecaptured with streptavidin-coated magnetic beads, washed to removeunbound template, and then PCR amplified. The amplified products werethen SPRI cleaned using AMPURE beads and the entire process wasrepeated. The final PCR cleanup step yielded stock captured librariesthat were ready for dilution, QC, and normalization.

Further Quantification

After completing the target enrichment process, the enriched librariesare quantified using capillary electrophoresis (CaliperGX) or PCR-basedmethods (droplet digital PCR, quantitative PCR). Enriched libraries arethen normalized to a fixed concentration and loaded onto a nextgeneration sequencing instrument (e.g., Illumina HISEQ 2000/2500).

In one example, an Agilent Bioanalyzer 2100 was used to quantifysequencing libraries that were prepared from cell-free plasma DNA usingNEBNEXT biochemistry and subsequently double captured with an oncologyprobe panel. Specifically, libraries were analyzed to determine averagefragment size distribution and concentration via gel electrophoresis ona micro fluidic platform. The average fragment size of each capturedlibrary was determined using smear detection parameters and theconcentration was calculated by integration of the electropherogramoutput. The calculated concentration was used in a subsequentnormalization process prior to clustering and sequencing.

Sequencing

Sequencing by synthesis is performed using paired end sequencing, forexample. Libraries are sequenced for about 100 to 150 cycles for each ofthe paired reads.

In one example, sequencing was performed on the Illumina HISEQ 2500instrument. Illumina's sequencing by synthesis technology uses areversible terminator-based approach that is able to detect single basesas they are incorporated into a growing DNA strand. Afluorescently-labeled terminator is imaged as each dNTP is added andthen cleaved to allow incorporation of the next base. All fourreversible terminator-bound dNTPs are present at each cycle so a naturalcompetition between the bases minimizes incorporation bias. After eachround of synthesis the clusters are excited by a laser emitting a colorthat identifies the newly added base. The fluorescent label and blockinggroup are then removed allowing for the addition of the next base. Thisbiochemistry allows for a single base to be read each cycle. Using theHISEQ 2500 sequencer and reagent kits from Illumina for this example,the clusters on a flow cell were used as templates to generate paired150 base pair sequencing reads of ˜50 million uniquely aligned sequencesper sample (when assayed in 6-plex).

Data Analysis

Sequencing reads are aligned to a reference genome with one or moredistinct parameter settings. After alignment, certain processesdescribed herein are utilized to evaluate various types of geneticalterations (e.g., single nucleotide alterations, insertions/deletions,fusions, and copy number alterations).

Example 2 De-Multiplexing, Alignment, Read Group Generation andConsensus Making

In this Example, nucleotide sequence reads were de-multiplexed, aligned,and assigned to read groups. A consensus was generated as describedbelow.

De-Multiplexing and Alignment

The purpose of the process described below is to distribute readsaccording to sample, extract the single molecule barcode (SMB), and toalign the reads to a reference genome. This process requires a completeIllumina sequencing run as input. The output contains various FASTQfiles and BAM alignment files.

In this Example, BCL convert was run on sequence read data prepared asdescribed in Example 1 using a script provided by Illumina. Thisresulted in FASTQ reads. A custom Perl script was applied to match readpairs to sample IDs using the sample index read. In certain instances,de-multiplexing was performed using a custom de-multiplexing process,referred to as a “demultiplexer.” The demultiplexer first parsed asample sheet to associate index values to sample names. Thedemultiplexer then proceeded to read in two or three fastq files (R1,R2, R3 optional) as generated by bcltofastq1.8. It assumed that eachrecord in R1 file, had a corresponding record in R2 in the sameposition, and a corresponding record in R3 in the same position forpaired end data. Given this assumption, the demultiplexer processed eachrecord from R1, R2, and R3 (optional) as a set. The demultiplexerinterpreted R2 as the “index” for R1 and R3 (mate of R1), but only usedpart of the read as the actual index and the other part as a randombarcode. The barcode (for the sample index) was appended to the headerof R1 and R3 for downstream processing whereas the index part wasfuzzily matched against the list of indexes as determined by the samplesheet. This was the de-multiplexing part. If a match occurred, theupdated records were written to a fastq file matching the sample towhich the index belongs. Otherwise, the record was placed in anundetermined_index file. The random barcode (i.e., the SMB) sequence wassplit from the sample index read and concatenated to the read name ofboth paired end reads. The barcode base quality values also wereconcatenated to the read names. Reads that did not pass an Illuminachastity filter were stored in separate fastq files. Trimmomatic (0.32)was applied to remove large adapter sequences remaining on each read.The trimmed reads were aligned using BWA mem (0.7.12) with defaultparameters to HG19. The alignments were converted to BAM format, thensorted and indexed using Samtools (1.1).

Read Groups

The purpose of the process described below is to mark duplicate readsand generate read groups. Read groups generally are a collection ofreads with similar start (i.e., the start of the corresponding DNAtemplate), end (i.e., the end of the corresponding DNA template), andbarcode. This process requires a sorted and indexed BAM file, with SMB(single molecule barcode) as part. It is run on each chromosomeindependently. It generates a new BAM file with duplicate reads marked,and read group IDs associated with each read. This process also splitson-target reads, off-target reads, and ambiguous reads into separate BAMfiles.

In this Example, a few filters were applied on the raw aligned dataprovided above. Barcodes and/or indexes with ambiguous nucleotides,ambiguously aligned reads, and discordantly mapped reads were filteredout. For example, reads with an SMB or sample index having a single basewith a base quality score of less than 14, or two or more bases with abase quality score of less than 21, were filtered out. Then, using acustom set of Perl scripts, PCR duplicates were identified using therandom single molecule barcode (SMB) associated with each read. The SMBwas parsed from each aligned read and a molecule signature was createdby concatenating the SMB with the chromosome, start position of thetemplate, and end position of the template. Reads having identicalmolecule signatures were flagged as duplicate reads by adjusting the bitflag in the alignment file and were given a unique read group numericalidentifier. Read groups which shared the same SMB and were within 5bases of each other from either the start or end of the template werecollapsed together by marking them with the same read group identifier.Read groups with similar SMBs were checked and read groups with SMBsthat have an edit distance less than two were collapsed (reads assignedto a read group have the same SMB (zero mismatches) or nearly the sameSMB (edit distance of 1)). The final output from these scripts was aduplicate marked alignment file with one entry for each on-target read.Intermediary files also were outputted. One file contained signaturesfor each molecule and the number of times a molecule signature wasobserved. Another file contained the number of reads per multiplicity ofread groups.

Consensus

The purpose of consensus making is to collapse SMB read groups tocompile a sequence representation of the original template. This processgenerates a single read which will represent duplicate reads that weregrouped into read groups. In other words, this process generates aconsensus read representing a collection of reads in a single readgroup. This process uses a marked duplicate chrom file (i.e., a BAM filewith the read group id as the first column) and outputs a sorted BAMfile with consensus reads. A call file also is outputted which containsthe nucleotide count at each position in the panel.

In this Example, consensus sequences were made for each set of readgroups originating from one template in the original sample. Consensusreads were generated for each end of a template; and for each pair ofconsensus reads having an overlapping region, nucleotide identityagreement for each base in the overlapping region was assessed. Ifnucleotide identity agreement was not present for a base in theoverlapping region, the base with the higher quality was selected. Then,for each position in a template covered by any of the reads, the totalnumber and identity of the nucleotides at that position was determined,and their total qualities were assessed. If a position had >=90% of thecount of the nucleotides and >=90% of the quality of the nucleotidesagreeing on the same call, then that base and the mean quality for thatletter was the output. Otherwise, an “N” with base quality “#” was theoutput. The base calls were then tallied for each position.

QC Metrics

Table 1 provides a description of certain quality control (QC) metricsassessed for each sample run. Certain terms referred to in Table1*include: panel (all positions overlapped by a capture probe); paddedpanel (panel with an additional 250 bases on either side of captureprobes); singleton (a consensus read group that has only one read pair);doubleton (a consensus read group that has two read pairs); consensuscoverage (coverage derived from sequences that are themselves a readgroup consensus; does not include singletons or doubletons); consensus1-2 ton coverage (coverage derived from sequences that are themselves aread group consensus, including singletons and doubletons; and rawcoverage (coverage from all input reads without consensus or duplicatemarking).

An additional QC metric may be employed using the nonrandomoligonucleotide adapters of the present application. The sequences ofthe set of the nonrandom oligonucleotide adapters provided in theligation reaction are known. Adapter-ligated nucleic acid templates thatconsist of at least one adapter having a nucleotide sequence that is notone of the known adapter set are removed from any additional sequencingor counting analysis.

The QC metrics file contains the following metrics. Values listed hereare example values from an arbitrary sample and are for illustrativepurposes only.

TABLE 1 Metric Value Description Total Reads 284312328 Total reads inputinto the pipeline, typically chastity filtered reads Aligned Reads282922663 Number of reads that align to the genome Discordant Reads226331 Number of reads that are discordant On-Target Reads 245672560Number of reads that align and overlap the padded panel Alignment Rate0.995112188733511 Fraction of reads that align to the genome DiscordantRate 0.000796064671525605 Fraction of reads that are discordant betweenread 1 and read 2 On-Target Rate 0.864093941082991 Fraction of readsthat align and overlap the padded panel Mean Raw Coverage 73,881.17Average raw panel coverage Median Raw Coverage 72,508 Median raw panelcoverage 10X Raw Coverage 0.999973028939947 Fraction of padded panelthat has at least 10X raw coverage 200X Raw Coverage 0.998783305513184Fraction of padded panel that has at least 200X raw coverage 500X RawCoverage 0.998306816785589 Fraction of padded panel that has at least500X raw coverage 1000X Raw Coverage 0.997944205867105 Fraction ofpadded panel that has at least 1000X raw coverage Standard Deviation32,948.79 Standard deviation of raw padded panel coverage Raw CoverageMean Consensus 2,252.04 Average consensus padded panel coverage CoverageMedian Consensus 1,249 Median consensus padded panel coverage Coverage10X Consensus 0.938361415544044 Fraction of padded panel with at least10X Coverage consensus coverage 200X Consensus 0.689131411644163Fraction of padded panel with at least 200X Coverage consensus coverage500X Consensus 0.604114015720306 Fraction of padded panel with at least500X Coverage consensus coverage 1000X Consensus 0.530249664006354Fraction of padded panel with at least 1000X Coverage consensus coverageStandard Deviation 2,528.09 Standard deviation of consensus padded panelConsensus Coverage coverage Mean Consensus With 2,507.85 Average ofconsensus 1-2 ton padded panel 1-2 ton Coverage coverage MedianConsensus With 1,406 Median of consensus 1-2 ton padded panel 1-2 tonCoverage coverage 10X Consensus Wtih 0.948465279291839 Fraction of panelwith at least 10X consensus 1-2 ton Coverage 1-2 ton padded panelcoverage 200X Consensus With 0.707896899850863 Fraction of panel with atleast 200X consensus 1-2 ton Coverage 1-2 ton padded panel coverage 500XConsensus With 0.61798674040614 Fraction of panel with at least 500Xconsensus 1-2 ton Coverage 1-2 ton padded panel coverage 1000X ConsensusWith 0.545143932205541 Fraction of panel with at least 1000X consensus1-2 ton Coverage 1-2 ton padded panel coverage Standard Deviation2,802.25 Standard deviation of consensus 1-2 ton padded Consensus Withpanel coverage 1-2 ton Coverage Mean Consensus 4,936.97 Mean consensuspanel coverage Coverage Panel Median Consensus 4,786 Median consensuspanel coverage Coverage Panel 10X Consensus 0.998078022698492 Fractionof panel with at least 10X consensus Coverage Panel coverage 200XConsensus 0.996036296032404 Fraction of panel with at least 200Xconsensus Coverage Panel coverage 500X Consensus 0.991503782583054Fraction of panel with at least 500X consensus Coverage Panel coverage1000X Consensus 0.981390948744278 Fraction of panel with at least 1000Xconsensus Coverage Panel coverage Standard Deviation 2,225.5 Standarddeviation of panel consensus coverage Consensus Coverage Panel MeanConsensus With 5,479.65 Average of consensus 1-2 ton panel coverage 1-2ton Coverage Panel Median Consensus With 5,307 Median of consensus 1-2ton panel coverage 1-2 ton Coverage Panel 10X Consensus Wtih0.998194778328958 Fraction of panel with at least 10X consensus 1-2 tonCoverage 1-2 ton coverage Panel 200X Consensus With 0.996551218300098Fraction of panel with at least 200X consensus 1-2 ton Coverage 1-2 toncoverage Panel 500X Consensus With 0.992946762426242 Fraction of panelwith at least 500X consensus 1-2 ton Coverage 1-2 ton coverage Panel1000X Consensus With 0.984923554999386 Fraction of panel with at least1000X consensus 1-2 ton Coverage 1-2 ton coverage Panel StandardDeviation 2,470.14 Standard deviation of consensus 1-2 ton panelConsensus With coverage 1-2 ton Coverage Panel Mean Probe Unique2,060.08 Average of average probe consensus 1-2 ton coverage CoverageMedian Probe Unique 1,807.12 Median of average probe consensus 1-2 toncoverage Coverage Standard Deviation 1,144.92 Standard deviation ofaverage probe consensus Probe Unique Coverage 1-2 ton coverage Reads PerSMB 8.10385332610447 Average number of read pairs in a consensus readgroup Singleton Rate 0.0686965529607867 Fraction of read groups that arecomposed of singletons Doubleton Rate 0.0314754249341031 Fraction ofread groups that are composed of doubletons Median Upsample 17 Themedian number of bases sampled to observe all Metric available barcodes.This is repeated 1000 times from randomly chosen starting points.

Example 3 Identification of Single Nucleotide Alterations

In this Example, single nucleotide alterations were detected byanalyzing reads and consensus sequences generated using methodsdescribed in Examples 1 and 2, except where otherwise noted.

VCF Maker

A pileup of reads was generated post consensus to generate allelic countat each position in the probe panel described above. The count of uniquebases and qualities were tallied independently for a given position. Incertain instances, the position based counting information was convertedto a variant call format (VCF). The overall process for VCF conversionincluded the following steps:

1) Tally consensus base counts at each position

2) Calculate allele depth and fraction

3) Annotate each position with external data

-   -   a. Gene information    -   b. Effect e.g. intergenic_region, intron_variant    -   c. Impact e.g. modifier, low, high    -   d. Amino acid change (if any)    -   e. Observed population frequencies in:        -   i. UK10k database        -   ii. dbNSFP 1000 genomes database        -   iii. dbSNP        -   iv. ESP6500

4) Annotate each position with internal data

-   -   a. List of actionable SNPs    -   b. Mappability scores    -   c. Homopolymer rate

5) Only positions covered by a probe reported

6) General sample level metrics embedded in the VCF header

7) True positives and false positives not identified

Described below is the script used to convert position-based countinginformation output by the methods above into VCF (variant call format)and MAF (mutation annotation format) with position-based annotation. VCFis a text file format containing meta-information lines, a header line,and data lines containing information about a position in a genome. AMAF file (.maf) is a tab-delimited text file that lists mutations. Theoperation of the script itself and the external data sources used toannotate the resulting VCF are described.

Certain terms referred to in the general script algorithm include:multiplicity (the number of raw molecules that are combined to form asingle consensus molecule, which is half the number of raw reads forpaired end sequencing); and singleton (a consensus molecule with amultiplicity of 1).

Generally, the script tallies up all the consensus base counts at eachposition, and calculates total allele depth and fraction, then annotatesthe position based on certain external resources and outputs the resultsin VCF and MAF format. The primary function is reformatting the data inindustry standard formats.

For certain applications, Illumina uses 8 quality bins (numbered 0through 7) to describe base qualities. When considering consensuscounts, quality bins 5, 6 and 7 are included, corresponding to qualityscores of >=30. When referring to the probe panel described above, onecan refer to regions that are both covered by a targeted probe(inProbe=1), or are adjacent to a probe (inProbe=0). By default an entryin the VCF and MAF files is generated for every position whereinProbe=1, and no positions where inProbe=0.

The general script algorithm includes:

1) Read in external files with fixed position-based annotation:

-   -   a. List of actionable SNPs    -   b. List of mappability scores for each position in the panel    -   c. List of homopolymer rate for each position in the panel    -   d. List of external database annotations        -   i. Gene information        -   ii. Effect e.g. intergenic_region, intron_variant        -   iii. Impact e.g. modifier, low, high        -   iv. Amino acide change (if any)        -   v. Observed population frequencies in:            -   1. UK10k database            -   2. dbNSFP 1000 genomes database            -   3. dbSNP            -   4. ESP6500    -   e. Read in multiplicity weights file (if applicable, see below)

2) Read in the consensus counts file line by line

-   -   a. Collect allele counts, fraction, total counts    -   b. Simultaneously read in the bias stats file line by line

3) Generate the VCF output entry.

-   -   a. ID is the dbSNP rslD when available, otherwise “.”    -   b. REF is the reference base    -   c. ALT is all non-reference bases that have a non-zero consensus        count    -   d. QUAL is always “.”    -   e. FILTER one or more of:        -   i. MINCOV100: did not meet minimum coverage of 100        -   ii. MINCOV500: did not meet minimum coverage of 500        -   iii. MINALT2: did not meet minimum alternate depth of 2        -   iv. MINALT4: did not meet minimum alternate depth of 4        -   v. PASS: meets all filters    -   f. INFO        -   i. Contains all the information relevant to the position            read in from external files in step #1 above    -   g. FORMAT        -   i. GT: Genotype, a “1” delimited string of indices of all            present alleles        -   ii. DP: Total consensus depth at that position        -   iii. AD: Allele depth for ref and alt in order listed        -   iv. AF: Allele fraction for ref and alt in order listed        -   v. SF: Singleton fraction for ref and alt in order listed        -   vi. SB: Strand bias for ref and alt in order listed        -   vii. EB: End bias for ref and alt in order listed        -   viii. IB: Indel bias for ref and alt in order listed    -   h. <SAMPLE> entry contains the actual values for items defined        in FORMAT

4) Generate the MAF output entry

-   -   a. Hugo_Symbol: Gene    -   b. Entrez_Gene_id: “.”    -   c. Center: SQNM    -   d. HCBI_Build: hg19    -   e. Chromosome    -   f. Start_Position: Current position    -   g. End_Position: Current position    -   h. Strand: “+”    -   i. Variant_Classification: Mutation severity e.g. high or low    -   j. Reference_Allele: Reference allele    -   k. Tumor_Seq_Allele1: Alternate allele with highest fraction (if        any)    -   l. TumorSeq_Allele2: Alternate allele with second highest        fraction (if any)    -   m. dbSNP_RS: dnSNP rsID (if available)    -   n. dnSNP_Val_Status: “.”    -   o. Tumore_Sample_Barcode: “.”    -   p. Matched_Norm_Sample_Barcode: “.”    -   q. Match_Norm_Seq_Allele1: “.”    -   r. MatchNorm_Seq_Allele2: “.”    -   s. Tumor_Validation_Allele1: “.”    -   t. Tumor_Validation_Allele2: “.”    -   u. Match_Norm_Validation_Allele1: “.”    -   v. Match Norm Validation Allele2: “.”    -   w. Verification_Status: “.”    -   x. Validation_Status: “.”    -   y. Mutation_Status: “.”    -   z. Sequencing_Phase: “.”    -   aa. Sequence_Source: “.”    -   bb. Validation Method: “.”    -   cc. Score: “.”    -   dd. BAM File: “.”    -   ee. Sequencer: IlluminaHiSeq    -   ff. Tumor_Sample_UUID: “.”    -   gg. Matched_Norm_Sample_UUID: “.”    -   hh. (Columns below this point are custom column additions)    -   ii. Tumor_Seq_Allele3: Alternate allele with third highest        fraction (if any)    -   jj. InProbe: Whether or not the position is within a targeted        probe    -   kk. Total_Depth: Total consensus depth at that position    -   ll. Reference_Depth: Depth of reference allele    -   mm. Alt_Depth1, Alt_Depth2, Alt_Depth3: Alternate allele depths    -   nn. Reference_Frac: Allele fraction of reference    -   oo. Alt_Frac1, Alt_Frac2, Alt_Frac3: Alternate allele fractions    -   pp. Reference_SF, Alt_SF1, Alt_SF1, Alt_SF3: Singleton fraction        of ref and alts    -   qq. Reference_SB, Alt_SB1, Alt_SB2, Alt_SB3: Strand bias of ref        and alts    -   rr. Reference_EB, Alt_EB1, Alt_EB2, Alt_EB3: End bias of ref and        alts    -   ss. Reference_IB, Alt_IB1, Alt_IB2, Alt_IB3: Indel bias of ref        and alts    -   tt. Actionable: Annotation of actionable mutation (if any)    -   uu. Mappability: Calculated mappability rate at that position    -   vv. Homopolymer: Calculated hompolymer rate at that position    -   ww. Population_Frequency: Population frequency in        above-mentioned databases    -   xx. Sample_ID: Internal sample ID of given sample    -   yy. Context: Reference sequence context of the position    -   zz. Adjacent_Variant: Whether or not the adjacent positions have        non-reference allele counts        -   i. m1: Only the previous position has non-reference alleles        -   ii. p1: Only the next position has non-reference alleles    -   iii. m1p1: Both previous and next positions have non-reference        alleles

5) Calculate singleton fraction statistics considering all referencealleles with coverage >=500. This information is embedded in the headerof the VCF and MAF files

-   -   a. Average    -   b. Standard deviation    -   c. Median    -   d. MAD

A script for position variant calling is presented below:

rm(list=ls( )) gc( ) library(MASS) library(parallel) arg=commandArgs( )dirOut = as.character(unlist(strsplit(arg[ pmatch(″--dirOut″,arg)],″=″))[2]) sampleNm.tr = as.character(unlist(strsplit(arg[pmatch(″--sampleNmTr″,arg)], ″=″))[2]) # a vector of training samplenames (e.g. RDSRs) sampleNm.ts = as.character(unlist(strsplit(arg[pmatch(″--sampleNmTs″,arg)], ″=″))[2]) # testing sample names (e.g.RDSRs) mc_cores = as.numeric(unlist(strsplit(arg[pmatch(″--mc_cores″,arg)], ″=″))[2]) # number of threads outRFile =as.character(unlist(strsplit(arg[ pmatch(″--outRFile″,arg)], ″=″))[2]) #full dir to output file inRFile = as.character(unlist(strsplit(arg[pmatch(″--inRFile″,arg)], ″=″))[2]) # full dir to input fileload(inRFile) # load in loci (a vector of loci of interests),sampleNm.tr ( a vector of training sample names, e.g. RDSRs),sampleNm.ts (testing sample names) and x.list ( a list of lociinfo--each sublist corresponds to one locus. Rows are samples andcolumns contains at least the following info: DP, AF.alt and AD.altobtained from vcf files.), names(loci) = loci nLoci = length(loci)############# 1) Variant calling/outlier detection based on mahaldistance AF.alt.tr.max = 0.05 DP.tr.min = 100 mahal.pvalue.thld = 0.01 #pvalue threshold loess.z.thld = 3 # zscore threshold detOutlier.mahal =T if(detOutlier.mahal){  p.chisq = 1 - mahal.pvalue.thld  df.chisq = 2 findOutlier.mahal <- function(locus){   x = x.list[[locus]][, c(″DP″,″AF.alt″)]   if(is.null(x) ) return(NULL);   if(nrow(x)==0)return(NULL);   selId = which(x[,″AF.alt″]<= AF.alt.tr.max & (x[,″DP″]>=DP.tr.min) )   if (length(selId)==0) return(NULL)   x = log10(x)   x.tr= x[selId,][sampleNm.tr, ]   x.ts = x[sampleNm.ts, ]   mu = apply( x.tr,MARGIN=2, median, na.rm=T)   sigma = cov(x.tr,use=″pairwise.complete.obs″)   if ( class(try( mahalanobis(x.ts,center=mu, cov=sigma) ))==″try-error″ ){    return (NULL)    } else {    mahal = mahalanobis(x.ts, center=mu, cov=sigma)    }    mahal.pvalue= pchisq(q=mahal, df=df.chisq, lower.tail=F)    outId =which(mahal.pvalue < mahal.pvalue.thld )    rst = list(mahal=mahal,mahal.pvalue=mahal.pvalue, outId=outId, mu=mu, sigma=sigma)   return(rst)   }   detRst.mahal = mclapply(loci, mc.cores=mc_cores,FUN=function(locus){    detRst = findOutlier.mahal(locus)   return(detRst)   })  }  ############# 2) Variant calling/outlierdetection based on Loess regression  detOutlier.loess = T if(detOutlier.loess){   loess.span = 2   min.se = 0.008   featureNm.y =″AF.alt″   findOutlier.loess <- function(locus, featureNm.y = ″AF.alt″,loess.span=2, debug=F){    x = x.list[[locus]][, c(″DP″, ″AF.alt″,″AD.alt″)]    if(is.null(x) ) return(NULL);    if(nrow(x)==0)return(NULL);    x.tr = x[sampleNm.tr, ]    x.ts = x[sampleNm.ts, ]   xl.tr = x.tr[,″DP″]    yl.tr = x.tr[, featureNm.y]   names(xl.tr)=names(yl.tr)=sampleNm.tr    xl.ts = x.ts[,″DP″]    yl.ts= x.ts[, featureNm.y]    names(xl.ts)=names(yl.ts)=sampleNm.ts    selId= x.tr[,″AF.alt″]<= AF.alt.tr.max & (x.tr[,″DP″]>= DP.tr.min) & (!(is.na(xl.tr)|is.na(yl.tr) )) #    xl.tr = xl.tr[selId]    yl.tr =yl.tr[selId]    if(length(xl.tr)<=1 | length(yl.tr)<=1) return(NULL);   sortId = order(xl.tr)    xl.tr = xl.tr[sortId]    yl.tr =yl.tr[sortId]    regMethod = ″loess″ # default    if ( class(try(suppressWarnings( loess(yl.tr~xl.tr)) ))==″try-error″ ){     regMethod<- ″rlm″    } else {     suppressWarnings( tmp.lo.fit <-loess(yl.tr~xl.tr, span=loess.span, degree=2))     if ( all( is.na(tmp.lo.fit$residuals )) ) {      regMethod <- ″rlm″     } else {     suppressWarnings( tmp.pred.lo <-predict(tmp.lo.fit, se=T) )      if( all( is.na( tmp.pred.lo$se)) | any( is.infinite(tmp.pred.lo$se)) ) {      regMethod <- ″rlm″      }     }    }    if (regMethod==″rlm″){    lm.fit = rlm( yl.tr~xl.tr )     se <- abs(lm.fit$residuals)    myfit <- lm.fit    }else if (regMethod==″loess″) {     pred.lo<-predict(loess(yl.tr~xl.tr, span=loess.span, degree=2), se=T)     se <-pred.lo$se.fit     myfit<-pred.lo    }    yl.tr.mad = mad(yl.tr[xl.tr>300], na.rm=T )    se.med = median(se, na.rm=T)    se.med0 =se.med    if(se.med < min.se){     se.med <- min.se    }    err.tr =rep(se.med, length(xl.tr))    err.ts = rep(se.med, length(xl.ts))   xOutId = which( xl.ts>max(xl.tr) | xl.ts<min(xl.tr))   err.ts[xOutId] <- se.med    extroQuant = 0.05    tmp <- myfit$fit;tmp.left = quantile(tmp, na.rm=T, probs=1-extroQuant); tmp.right =quantile(tmp,  na.rm=T, probs=extroQuant)    fit.itpl = approx(xl.tr,myfit$fit, xout = xl.ts, yleft=tmp.left, yright=tmp.right)   ci.upper.ts = fit.itpl$y + loess.z.thld * err.ts    ci.lower.ts =fit.itpl$y - loess.z.thld * err.ts    z.ts = (yl.ts - fit.itpl$y) /err.ts # z score for each test point    outId = which( z.ts >loess.z.thld)    rst = list(xl.tr=xl.tr, yl.tr=yl.tr, myfit=myfit,yl.tr.mad=yl.tr.mad,       fit.itpl=fit.itpl, err.ts=err.ts,      z.ts=z.ts, outId=outId)    return(rst)   }   detRst.loess =mclapply(loci, mc.cores=mc_cores, FUN=function(locus){    detRst =findOutlier.loess(locus, featureNm.y, debug=F)    return(detRst)  }) }save(detRst.loess, detRst.mahal, file=outRFile)

Certain metrics were annotated as illustrated in the flowchart presentedin FIG. 9 and described below.

Mappability

Provided below is a summary of how mappability scores were calculatedfor individual positions in the assay panel described above. Mappabilityis a metric that can indicate how reliable mapping is when a sequence ismapped to a particular region of the genome. Mappability can benegatively impacted by elements such as repeat regions that make aunique alignment to the genome difficult.

Mappability scores were calculated using the following method:

1) Simulate 100 bp reads by extracting 100 bp sections of the genome.

-   -   a. These simulated reads span the entire panel    -   b. These simulated reads are staggered in 1 base increments

2) Mutate the simulated reads at every position in the following way:

-   -   a. 1 base mismatch (all mismatch bases)    -   b. 2 base mismatch (all mismatch base combinations)    -   c. 1 base insertion (all possible bases)    -   d. 2 base insertion (all possible base combinations)    -   e. 5 base insertion (all possible base combinations)    -   f. 10 base insertion (all possible base combinations)    -   g. 1 base deletion    -   h. 2 base deletion    -   i. 5 base deletion    -   j. 10 base deletion

3) Align original and mutated reads back to the genome using bwa mem,default parameters

4) Calculate the number of times the read aligned back to the originalposition (mapback)

5) Calculate the number of positions each read aligned to in the genome(althit)

6) Calculate mappability as mapback/althit

-   -   a. If all mutated reads mapped back to the original position        uniquely, mappability is calculated as N/N or “1”.

Weighted Homopolymer Rate

Provided below is a summary of how weighted homopolymer rates (WHR) werecalculated for individual positions in the assay panel described above.A homopolymer is a sequence of identical bases, like AAAA or TTTTTTTT,for example. The weighted homopolymer rate (WHR) of a sequence is ameasure of the frequency of homopolymers in the sequence.

According to the Broad Institute definition:

${WHR} = \frac{\sum_{i = 1}^{N}n_{i}^{2}}{N}$

where N is the number of homopolymers in the sequence, and the n_(i)'sare the homopolymers' lengths, so that the summation goes from 1 to N (Nis not the total length of the sequence).

For example:

TGATTCAAGCATTCGATC: This homopolymer-poor sequence has a WHR of(1+1+1+4+1+4+1+1+1+4+1+1+1+1+1)/15=1.6.

GGGTGCCCCCAAAATATT: This homopolymer-rich sequence has a WHR of(9+1+1+25+16+1+1+4)/8=7.25.

The lowest possible WHR of a sequence is 1; the highest possible is thesquare of the sequence length (if N=1). A randomly-generated sequencehas an expected WHR of 20/9≈2.222. Most genomes have WHRs higher thanthe random value, due to imbalances in GC-content and the presence ofjunk DNA.

Indel Bias, End Bias and Strand Bias

Provided below is a summary of how indel bias, end bias and strand biasscores were calculated for the various alleles and positions.

The method below refers to overlapping mate consensus, which is aconsensus base that comes from a section of the molecule where mate 1and mate 2 strands overlap. For example, if the physical molecule is 150bases long, and the reads are 150 bases, all of the bases should haveoverlapping mates. If the physical molecule is 100 bases long, and thereads are 150 bases long, only the middle 100 bases have overlappingmates. The 50 bases on either end come from a mate aligned to either the“+” or “−” strand of the genome.

Strand bias, indel bias, and end bias were calculated using thefollowing method:

1) Strand Bias

-   -   a. For each allele X where X is A, C, G or T:        -   i. Track the number of times the consensus is formed from            overlapping mates.            -   1. Xboth        -   ii. Track the number of times the consensus is formed from            non-overlapping mates aligned to the “+” strand            -   1. Xplus        -   iii. Track the number of times the consensus is formed from            non-overlapping mates aligned to the “−” strand            -   1. Xminus    -   b. For each allele X:        -   i. Calculate a p-value indicating whether or not the            relative counts of Xboth, Xplus and Xminus are different            than for the other three alleles. This is performed using            the fisher.test function in the R programming language.            -   1. XstrandStat

2) Indel Bias

-   -   a. For each allele X:        -   i. Track the number of times the consensus base is found            near an indel (within 3 bases). The tally is done in            consensus space, but the proximity to indels is defined by            the CIGAR strings from non-consensus aligned reads            -   1. Xindel        -   ii. Track the number of times the consensus base is not near            an indel            -   1. Xnolndel    -   b. For each allele X:        -   i. Calculate the fraction of consensus counts that are near            an indel            -   1. XindelStat

3) End Bias

-   -   a. For each allele X:        -   i. Track the number of times the consensus base is near the            end of the molecule (within 5 bases). This refers to the            physical molecule, which is bounded on either end by the two            mates            -   1. XnearEnd        -   ii. Track the number of times the consensus base is not near            the end of the molecule            -   1. XnearMiddle    -   b. For each allele X:        -   i. Calculate the fraction of consensus counts that are near            an end            -   1. XendStat

Position Specific Error Model

In certain instances, a position specific variant calling algorithm isapplied to position specific data generated as described above.Generally, the input to the algorithm is a list of loci (or filteredlist of loci) along with historical data pertaining to the loci.Typically, the algorithm can be run after vcf generation, GATK germlinefiltering and/or other basic filtering. Model based position specificnoise removal and signal detection is described below. Specifically, aposition specific classification model which can distinguish signal frombackground noise by utilizing information residing in a cohort ofsamples is described.

Fixed thresholds often are used for variant filtering and detection. Forexample, one conservative criterion may include requesting all reportedvariants have at least 5 alternative allele depth and more than 1%allele fraction. However, each locus in the genome may have differentcharacteristics in terms of sequence context, mappability, backgroundnoise level, etc. A position-specific threshold or position-specificmodel may distinguish signal or true variant from background noise.Filtering variants for each sample based on fixed thresholds also doesnot utilize information residing in a cohort of samples. Some truevariants may fail a fixed threshold, resulting in reduced sensitivity;and false variants may pass the threshold, resulting in reducedspecificity. By compiling information (e.g., allele depth, allelefraction, and the like) across multiple samples for a particularposition, a position-specific background distribution is generated. Forconsideration of a variant, its statistics (e.g., allele depth, allelefraction) can be compared to the background to verify whether thevariant is a true signal or not.

The first step is data preprocessing to parse and compile relevantinformation (such as AF (alternative allele fraction at a givenposition) and DP (consensus depth at a given position)) from input VCFfiles. The output is an R list variable and each sub-list containsdetailed information of one locus across different samples as specifiedin the sample sheet.

Two algorithms were developed to calculate the variant significance foreach sample based on a position-specific model developed using trainingdata. The training data was generated using normal buffy coat samplesand normal plasma samples. Including buffy coat samples into thetraining dataset not only increased the training data size but alsoeffectively extended the range of data observations (in terms of depthand allele frequency distribution).

The first algorithm is regression based. It was developed based on theobservation that the AF distribution can be dependent on DP and thetrend may vary for different positions. Therefore, for each givenposition (positions are processed in parallel), the algorithm tries tofit a loess model (curve) for the predictor DP and response variable AFusing training data pertinent to the locus. The degree of loesssmoothing level and number of model polynomials are both set to twobased on empirical results. If loess fitting fails (typically due toinsufficient training data for certain positions), the algorithm thenswitches to a robust linear fit to simplify the regression problem.Next, for each variant (one sample data point in the DP and AFclassification plane of the position), its z-score is calculated as:z=(AF-μ)/e, where AF is the alternative allele fraction for that sample,μ is the predicted response based on the predictor DP (depth of thesample), and e is the median standard error of the loess regressionmodel (or median residual error of the linear regression model). Ahigher z-score suggests that the variant stands out from the backgroundDP and AF distribution and is more likely to be a true mutation. Incertain instances, samples may show high z-scores in a noisy position(e.g., a known dbSNP position or a position in a region that has lowmappability). In such instances, visual inspection may be used tofurther rule out false positives with high z-scores.

The second algorithm is a 2-dimensional classification approach, whichutilizes the multivariate distribution of AF and DP. The values of AFand DP are first transformed logarithmically to obtain the desired “lognormal” property. Then, for each given position the algorithm calculatesthe distribution center and covariance of AF and DP using training datapertinent to the position. Next, for each variant (one sample data pointin the DP and AF classification plane specific to the position), itsmahalanobis distance from the distribution center is computed. The pvalue for the mahalanobis distance is then calculated as the tailprobability of a chi square distribution with degree of freedom equal to2. A smaller p value suggests that the variant stands out from thebackground DP and AF distribution and is more likely to be a truemutation. In certain instances, samples may show low p values in a noisyposition. In such instances, visual inspection may be used to furtherrule out false positives with low p values. This approach can beextended to a higher dimension, incorporating more pertinent featuressuch as end bias, indel bias and singleton fraction, which may providedifferent aspects for distinguishing true mutations from falsepositives.

Filtering

A filtering step was included in the methods discussed herein in Example4. Described below are methods and steps for 1) annotating variants (SNVand INDEL) produced by methods described above in VCF format; 2) callinggermline variants in buffy coat samples in a cohort; and 3) flagging andfiltering false somatic variants identified by the methods describedabove in plasma cfDNA. Methods described below are in the context of astudy cohort which included a set of plasma cfDNA samples and theirmatched buffy coat.

Certain terms used in the methods below include: CNV (copy numbervariant); GVCF (genomic variant call format); INDEL (short insertion anddeletion, e.g. <100 bp); SNV (single nucleotide variant); VCF (variantcall format); and variant classification (function annotations specifiedby HGVS (Human Genome Variation Society) implemented in snpEff (geneticvariant annotation and effect prediction toolbox). For variantannotation, open source snpEff/snpSift suite was used. For germlinevariant calling, GATK (genomic analysis toolkit, Broad Institutesoftware) was used. Flagging and filtering variants was achieved usingunix shell and R scripts.

Variant Annotation

For SNV annotation, a pre-computed SNV annotation file containing eachposition on the panel was created and used to annotate variants duringthe VCF making step. The pre-computed annotation file was generatedusing snpEff/SnpSift suite. For INDELs, annotation was done after VCFwas generated by the methods above using snpEff/snpSift suite.Annotation databases (VCF format) were downloaded from public sites,sorted and indexed. These included: dbSNP, COSMIC coding mutation V68,ESP6500, UK10K, ClinVar.

Germline Variant Calling

GATK variant calling was developed. Sequential steps for variant callinginclude: mark duplicates, re-alignment around know INDELs, recalibratebase call, variant call by HaploTypeCaller (EMIT_ALL_SITES option). AGVCF file was generated for each sample. All samples in the cohort werejointly called using GATK “GenotypeGVCFs” to create a VCF file, andannotation was done using snpEff/snpSift suite. Variants that have DP(consensus depth at a given position) >=30, GQ (genotype quality) >=99,AD (allelic depth) >1, and AF (alternative allele fraction at a givenposition) >=5% were designated as germline mutations. Total coverage foreach callable site was kept for somatic mutation filtering in next step.

Flag and Filter to Identify Somatic Mutation in cfDNA

Four categories of metrics were used to flag and filter variants. Thefirst category is variant quality including total depth, allele depth,exceed sample-specific error rate, buffy coat coverage for the variantsite, and site-specific noise level. The second category is variantcontext characteristics, which include end bias, indel bias, homopolymerrate, cluster of SNPs, reads start/stop diversity, genomic difficultregions. The third category is healthy population status; variantswith >0.1% in any of the normal databases (dbSNP/1000 Genome, UK10K,ESP6500) were considered germline mutations. The fourth category offlags is classification and function annotation of the variant bysnpEff/snpSift suite.

Most metrics were generated using scripts described above, and packagedinto “INFO” field in VCF file output. Exceptions include site-specificnoise level, buffy coat coverage, reads start/stop diversity (acustomized Perl script was created to tally start/stop genomic locationof a read that harbors the variant).

Cutoffs and steps are listed below:

1. Remove private germline variants that are identified in buffy coat;major alternative allele is considered.

2. Remove variant with AD<2, end bias=1, indel bias=1, homopolymerscore>=20, matched buffy coat coverage<30.

3. Flag variants>0.1% in at least three normal population databases, andvariants that are dbSNP-only without COSMIC records.

4. Flag variants with following fields:

-   -   a. AF exceeds sample-specific error rate    -   b. Site-specific variant call significance: p<0.05 and/or z>=3    -   c. Variant classification and impact annotations by snpEff    -   d. Genomic difficult regions: low complexity region, genome        super duplicated region, SQNM.black list    -   e. Start/stop diversity of mutation harboring reads>1

All metrics were collected into VCF and MAF file, flagging and filteringwere done using R script.

Example 4 Sample Report

In this Example, certain methods described in the above Examples wereused to identify single nucleotide somatic alterations in a subject.

A test designed to detect specific DNA alterations comprising singlenucleotide variants, insertions and deletions, copy number variations,and fusions in 134 cancer-related genes was performed on a plasma samplefrom a 95 year-old subject having a diagnosis of non-small cell lungcancer. The collection of genes assayed by the test is shown in Table 2below. Circulating cell-free DNA was isolated and purified from theplasma component of anticoagulated whole blood for detection of somaticDNA alternations. Additionally, genomic DNA was isolated and purifiedfrom the buffy coat for detection of germline DNA alterations. GenomicDNA libraries were prepared and used to determine DNA alterations bynext generation sequencing (NGS). Bioinformatic methods weresubsequently used to subtract the germline alterations from the somaticalterations. The assay was analytically validated in a research settingacross six variant frequencies. The demonstrated sensitivity was >78%for variant allele frequencies >0.5%. Specificity was >99% for allvariant levels at clinically actionable genomic loci.

TABLE 2 Genes represented in probe panel ABL2 CDKN2A FGFR3 MLH3 PIK3CGAKT1 CDKN2B FGFR4 MPL PIK3R1 AKT2 CSF1R FLT1 MSH2 POLE AKT3 CSF3R FLT3MSH3 PTCH1 ALK CTNNB1 GATA3 MSH6 PTEN APC DDR2 GNA11 MTOR RAC1 AR DNMT3AGNAQ MYC RAF1 ARAF EGFR GNAS MYCL1 RB1 ARID1A EML4 HIF1A MYCN RET ATMEPHA2 HNF1A MYD88 RHEB AURKA APHA3 HRAS NCOA4 RHOA AURKC ERBB2 IDH1 NF1RIT1 AXL ERBB3 IDH2 NF2 ROS1 BAP1 ERBB4 IGF1R NFE2L2 SMO BRAF ESR1 JAK3NKX2-1 SETD2 BRCA1 EWSR1 KDR NOTCH1 SMAD4 BRCA2 EZH2 KEAP1 NOTCH1SMARCB1 BTK FANCA KIT NPM1 SRC CBL FANCD2 KRAS NRAS STK11 CCND1 FBXW7MAP2K1 NTRK1 TERT CCND2 FGF3 MAP2K2 NTRK2 TET2 CCNE2 FGF10 MAP2K3 NTRK3TP53 CD274 FGF5 MAPK1 PDGFRA TRIM33 CD74 FGF6 MCL1 PDGFRB TSC1 CDH1 FGF8MDM2 PIK3CA TSCD CDK4 FGFR1 MET PIK3CB VHL CDK6 FGFR2 MLH1 PIK3CD

An initial plasma sample was collected from the subject (day 1; testindex 1) and subsequent collections were performed after about 1 month(day 28; test index 2) and again after about 6 months (day 198; testindex 3). Alterations detected in the samples are presented in Table 3below and an alterations trend for the different sample collections isshown in FIG. 10 .

TABLE 3 Allele Functional Gene Type Mutation fraction (%) impact KIT SNVV530I 5.61 gain PIK3CA SNV R93W 0.23 gain HRAS SNV G12S 1.78 gain PTENSNV R173C 2.69 loss TET2 SNV Q916* 0.38 loss VHL SNV E70K 0.26 normal

Descriptions of the genes provided in Table 3 are presented below.

KIT encodes a receptor tyrosine kinase that is expressed on a widevariety of cell types. The ligand for KIT is stem cell factor whichactivates downstream signaling pathways, including the PI3K-AKT-mTOR,RAS-RAF-MEK-ERK, and STAT3 pathways, all of which have a role in cellgrowth and survival. KIT mutations are found in more than 80% ofgastrointestinal stromal tumors (GIST). KIT mutations also occur inapproximately 20% of acute leukemias and 20% of genital tract cancers(COSMIC).

PIK3CA encodes the catalytic subunit of phosphatidylinositol 3-kinasethat belongs to a family of lipid kinases. These kinases regulate adiverse range of cellular processes including cell proliferation,adhesion, survival, and migration. Mutations in PIK3CA stimulatedownstream AKT-mTOR signaling pathways, thereby promoting growth-factorindependent growth, cell invasion and metastasis. PIK3CA mutations mayoccur in multiple malignancies, including approximately 25% of gastric,4% of lung, 25% of breast, and 20% of colorectal cancers. GermlinePIK3CA mutations may occur in Cowden syndrome.

HRAS belongs to the RAS oncogene family and encodes a GTP and GDPbinding protein with intrinsic GTPase activity. The protein cyclesbetween an inactive GDP-bound and active GTP-bound form, and is involvedin downstream receptor signaling critical for cell proliferation,survival and differentiation. HRAS mutations are found in multiplemalignancies including approximately 12% of skin cancers, 9% of salivarygland carcinomas, 8% of cervical carcinomas, and 3% of prostatecarcinomas (COSMIC). HRAS mutations are also frequent in cutaneoussquamous cell carcinomas and keratoacanthomas that develop in patientstreated with BRAF inhibitors. HRAS somatic mutations may occur incertain cases of acute myelogenous leukemia. Germline HRAS mutationscause Costello syndrome.

Somatic PTEN mutations may occur in a broad range of cancers, includingapproximately 40% of endometrial cancers, 11% of colorectal cancers, 10%of melanomas, 4% of ovarian cancer, and 3% of breast cancer (COSMIC).PTEN mutations have been found in approximately 11% of pediatric T-cellacute lymphoblastic leukemia (T-ALL) patients, and mutations areassociated with a negative prognosis. Germline PTEN mutations cause PTENhamartoma tumor syndrome and Cowden syndrome I. PTEN encodes aphosphatidylinositol-3,4,5-triphosphate 3-phosphatase. This proteinpreferentially dephosphorylates phosphoinositide substrates andnegatively regulates intracellular levels ofphosphatidylinositol-3,4,5-triphosphate in cells. It acts as a tumorsuppressor by negatively regulating the AKT-PKB signaling pathway bydephosphorylating phosphoinositides, thereby modulating cell cycleprogression and survival.

Somatic TET2 gene mutations have been reported in a variety of cancers,including approximately 18% of polycythemia vera cases, 4% ofendometrial carcinomas, 4% of colorectal carcinomas, and 2% of bladdercarcinomas (COSMIC). TET2 mutations may occur in approximately 13% ofprimary acute myeloid leukemia (AML) patients, and may be associatedwith an unfavorable prognosis in AML patients with intermediate riskcytogenetics. In certain instances, TET2 mutations may occur in about 7%of younger AML patients, with no impact of TET2 mutations on response totherapy and survival. TET2 mutations may occur in approximately 27% ofmyelodysplastic syndrome patients, and may predict response tohypomethylating agents. The TET2 gene encodes the enzyme methylcytosinedioxygenase that catalyzes the conversion of the modified genomic DNAbase methylcytosine to 5-hydroxymethylcytosine. Methylation of cytosinebases is an epigenetic modification that plays an important role intranscriptional regulation. The enzyme is involved in active DNAdemethylation, and also in myelopoiesis.

VHL encodes a protein that is part of a complex including elongin B,elongin C, and cullin-2, and possesses ubiquitin ligase E3 activity. Theprotein is involved in ubiquitination and degradation ofhypoxia-inducible-factor, a transcription factor involved in theregulation of gene expression by oxygen. The protein can target RNApolymerase II subunit POLR2G/RPB7. Somatic VHL mutations occur in abouthalf of patients with hemangioblastomas, and about half of clear cellrenal cell carcinomas. Germline VHL mutations cause von Hippel-Lindausyndrome, a dominantly inherited familial cancer syndrome thatpredisposes to a variety of tumors, including hemangioblastoma.

Example 5 Duplex Sequencing Evaluation

In this Example, certain methods described in the above Examples wereapplied to duplex sequencing data, and the error rates were compared tothe error rates for data generated using an existing method. Duplexsequencing data was generated as described above, with the exceptionthat adapters designed for duplex sequencing were used. The sequencingdata was processed as described above, with the exception that the stepsfrom read group to consensus were repeated, as illustrated in FIG. 7 .

Error rates for single strand consensus sequence (SSCS) and duplexconsensus sequence (DCS) data processed using an existing method arepresented in Table 4. “Index duplex” adapter molecules refer tononrandom oligonucleotide adapters. “Random duplex” adapter moleculesrefer to oligonucleotide adapter molecules comprising random basecompositions. The ratios of the number of adapter molecules provided inthe ligation reaction to nucleic acid templates is the same for both theindex duplex and random duplex reactions. The random duplex adapters areexpected to comprise approximately 1.7×10⁷ oligonucleotide species andthe index duplex adapters are expected to comprise approximately 2.9×10²oligonucleotide species.

TABLE 4 Raw SSCS DCS Sample Adapter error rate error rate error rate 1index duplex 8.82E−04 9.09E−04 5.27E−05 2 index duplex 8.63E−04 8.78E−045.42E−05 3 index duplex 8.59E−04 8.37E−04 5.34E−05 4 random duplex2.20E−03 2.31E−03 2.21E−05 5 random duplex 2.15E−03 2.13E−03 2.21E−05 6random duplex 1.83E−03 1.53E−03 2.49E−05

Single strand error rates were about 1.1 fold reduced relative to rawdata. Indexed duplex error rates were about 15 fold reduced relative toraw data. Random duplex raw error rates were about 100 fold reducedrelative to raw data (but raw error rates were much higher).

Error rates for data processed using the methods discussed herein arepresented in Table 5.

TABLE 5 Raw SSCS DCS Sample Adapter error rate error rate error rate 1index duplex 8.82E−04 4.23E−04 6.97E−06 2 index duplex 8.63E−04 4.18E−047.13E−06 3 index duplex 8.59E−04 3.93E−04 6.72E−06 4 random duplex2.20E−03 1.11E−03 6.60E−06 5 random duplex 2.15E−03 1.02E−03 7.08E−06 6random duplex 1.83E−03 5.81E−04 5.95E−06

SSCS error rate included only those “orphaned” SSCS data that were notincluded in duplexes. Thus, paired duplex reads were not used in thecalculation of SSCS error rate. (Some SSCS reads were not assigned to aduplex; these unassigned SSCS reads were used for this calculation.)

Single strand error rates were about 2.5 fold reduced relative to rawdata. Indexed duplex error rates were about 125 fold reduced relative toraw data. Random duplex raw error rates were about 300 fold reducedrelative to raw data (but raw error rates were much higher).Accordingly, the methods described herein generated data with about 3.5×to about 7.5× lower error rates than the error rates observed for datagenerated using an existing method.

Example 6 Manufacture of Sequencing Adapters Containing PredeterminedNon-Randomly Generated Bar Codes

Partially double-stranded Y-shaped nonrandom oligonucleotide adaptersare prepared prior to ligation of the nonrandom oligonucleotide adaptersto nucleic acid templates. (FIG. 11 ) Each strand of each nonrandomoligonucleotide adapter species may be synthesized and comprises apolynucleotide that is not complementary to the other strand, andcomprises an amplification primer binding sequence; for example, onestrand comprises a P5 sequence and the other strand comprises a P7sequence. These non-complementary regions may optionally comprise anindex sequence. Each strand of each nonrandom oligonucleotide adapterspecies also comprises a polynucleotide that has a nonrandom nucleotidesequence that is the reverse complement to the correspondingpolynucleotide on the other strand. The nonrandom nucleotide sequence ofthe present example is 8 nucleotides long, but may be of any appropriatelength, including, for example, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, or 20 nucleotides. These nonrandom nucleotide sequences maybe predetermined, for example, by selection using a computer, or may beobtained by fragmentation of natural DNA. By nonrandom is meant that thenucleotide sequence is predetermined before attachment of the adapter tothe nucleic acid template. The nonrandom nucleotide sequence may,however, be determined in advance using a computer program that randomlydesigns the sequence. The nonrandom oligonucleotide adapter sequencesalso have a single thymine overhang. Synthesized oligonucleotide speciesmay be pooled based on the non-complementary polynucleotide, forexample, one P5 and one P7 pool, then adapters may be prepared using,for example, the methods of Example 1.

In other examples, the nonrandom oligonucleotide adapters may beprepared by first synthesizing or obtaining a polynucleotide comprisinga nonrandom nucleotide sequence, copying the nonrandom nucleotidesequence using DNA polymerase, then ligating the resulting doublestranded oligonucleotide to a Y shaped tail, synthesized to comprise thenon-complementary polynucleotides discussed above in this example. Asingle thymine overhang is also attached to the double strandedoligonucleotide opposite the Y-shaped polynucleotides.

An example of a set of nonrandom oligonucleotide adapters is provided inTable 6 below. FIG. 12 provides an example of a schematic of thenonrandom oligonucleotide adapters ligated to the nucleic acid template.FIG. 12A provides a schematic of annealed nonrandom oligonucleotideadapters positioned adjacent to nucleic acid template DNA. FIG. 12B is aschematic of the nonrandom oligonucleotide adapters of FIG. 12A, ligatedto nucleic acid template DNA, and also depicts universal amplificationprimers annealed at each end of one of the strands of the construct.FIG. 12C provides a schematic of the template strand 1 libraryconstruct, obtained using the nonrandom oligonucleotide adapters/nucleicacid template construct and primers of the bottom strand of FIG. 12B.FIG. 12D provides a schematic of the template strand 2 library constructobtained using the nonrandom oligonucleotide adapters/nucleic acidtemplate construct and primers of the top strand of FIG. 12B, FIG. 12B.

In some examples, the second nonrandom oligonucleotide adapter speciescomprises the same molecular barcode as the first nonrandomoligonucleotide adapter species. In some examples, the second nonrandomoligonucleotide adapter species comprises a different molecular barcodethan the first nonrandom oligonucleotide adapter species.

In FIG. 12A and FIG. 12B, each strand of the oligonucleotides adapterincludes a universal sequence at one end. For purposes of the schematic,the top strand represents a first oligonucleotide species and the bottomstrand represents a second oligonucleotide species. The firstoligonucleotide species comprises at one end, for example at the 5′ end,a universal sequence. In the present example, the universal sequence isuniversal sequence 7B. The second oligonucleotide species comprises atone end, for example, the 3′ end, universal sequence 5B. A portion ofuniversal sequence 7B is not a reverse complement to a portion ofuniversal sequence 5B, and the two portions are not annealed. In theschematic, the first oligonucleotide species comprises a first molecularbarcode species (Molecular barcode 2), and the second oligonucleotidespecies comprises a molecular barcode species that is the reversecomplement of the first molecular barcode species (Molecular barcode2′). Each oligonucleotide species comprises a spacer region; the spacerregion of the first oligonucleotide species is the reverse complement ofthe spacer region of the second oligonucleotide species, with theexception that one of the oligonucleotide species further comprises aligation linker, for example, an A overhang (FIG. 12A).

FIG. 12B is a schematic of the nonrandom oligonucleotide adapters ofFIG. 12A, ligated to template DNA and hybridized to universalamplification primers. The Universal sequence 5A′ primer also includes asample identification barcode, shown as Sample ID′. Not shown in thedrawing are the universal amplification primers that may be used insequencing of the bottom strand, but are understood to be similar to theamplification primers shown in the top strand.

Example 7 Sequencing of Adapter-Ligated Nucleic Acid Templates

Nucleic acid templates are identified and grouped, where appropriate, byidentifying identical adapter sequences at one end or both ends. Wheretwo nucleic acid templates having different sizes or nucleotidesequences are ligated to identical adapter sequences, the nucleic acidtemplates are identified by mapping the position of the start and end ofeach template as discussed herein. Sequencing is performed essentiallyas discussed herein, with appropriate trimming of a number of cycles, “xcycles”, as needed so that the nonrandom oligonucleotide sequence iscopied with the insert (nucleic acid template sequence).

Example 8 Error Detection

Duplex sequencing using the nonrandom oligonucleotide adapters isperformed essentially as discussed herein.

In some embodiments, each nucleic acid template is tagged with anonrandom oligonucleotide adapter at one end and a standard sequencingadapter at one end. The nonrandom oligonucleotide adapter and thestandard sequencing adapter may be ligated to the nucleic acid templatesin the same ligation reaction, or in separate ligation reactions.Nucleic acid template sequences are grouped based on having matchingnonrandom barcodes in the nonrandom oligonucleotide adapter, and,optionally on also having matching standard sequencing adapters. Thesequences are aligned and compared. For each nucleic acid/nonrandomoligonucleotide adapter sequence group, there are two sets of amplifiedcopies. One set (A) includes copies having adapters comprising amolecular barcode, B; the other set (B) includes copies having adapterscomprising the complementary molecular barcode, B′. Sets (A) and (B) arepaired.

In some embodiments, each nucleic acid template is tagged with anonrandom oligonucleotide adapter at each end, adapter 1 and adapter 2.Nucleic acid template sequences are grouped based on having matchingnonrandom barcodes in both adapter 1 and adapter 2. The sequences arealigned and compared. For each nucleic acid/nonrandom oligonucleotideadapter sequence group, there are two sets of amplified copies. One set(A), includes copies having the orientation adapter 1/adapter 2, and theother set (B) includes copies having the orientation adapter2/adapter 1. For example, in set (A), adapter 1 may comprise themolecular barcode B1, and adapter 2 may comprise the molecular barcodeB2′, and in set B, adapter 1 may comprise the molecular barcode B1′ andadapter 2 may comprise the molecular barcode B2, where B1 and B2 are Bspecies, and B1′ has the complementary nucleotide sequence to B1, andB2′ has the complementary sequence to B2. Set (A) and set (B) arepaired.

For embodiments that comprise two nonrandom oligonucleotide adapters,one at each end, and for embodiments that comprise one nonrandomoligonucleotide adapter and one standard sequencing adapter, sequencingcalls are made by comparing the sequences within each set, and then bycomparing sequences in one set to the sequences obtained in paired setsin each group. In one illustrative example, where a mutation appears inonly one or a few members of one of the sets, it is likely to be asequencing error. Where a mutation appears in most of the members ofonly one of the sets, for example, in set A only, but the mutation doesnot occur in the other set (B) of the same group, then the mutation islikely to be only an artifact of PCR amplification. Where the samemutation occurs in 90% or more of the sequences in each set of the samegroup, a call is made that the mutation is a true mutation. Otherconsensus determination methods may also be used to call the truemutation.

Example 9 Counting Adapter-Ligated Nucleic Acid Templates

In some embodiments, methods are provided of counting nucleic acidmolecules, such as, nucleic acid templates. These counting methods maybe used, for example, to detect a genetic disorder, where the geneticdisorder is associated with a copy number alteration. Since each nucleicacid template with a nonrandom oligonucleotide adapter attached isamplified through the process of PCR as part of the library preparationprocess, duplicates of each template molecule are created. Aftersequencing and the nonrandom oligonucleotide adapter-ligated templatesare mapped to a genome, templates mapped to a particular region arecounted and the absolute or relative abundance of templates is used todetect copy number alterations. The nonrandom oligonucleotide adaptersof the present application allow for each duplicate of a templatemolecule to be counted once. Without the nonrandom oligonucleotideadapters, duplicates of a template molecule might be counted multipletimes, which increases the noise of the counting measurement. Duplexsequencing enables the accurate marking of original template molecules,thereby reducing the noise in counting methods by discriminating betweentrue molecules and amplified molecules.

Provided in Table 6 below are examples of nonrandom oligonucleotideadapters described herein and their sequences.

TABLE 6 Nonrandom oligonucleotide SEQ adapterOligonucleotide adapter Sequence ID Nos. Duplex_p5_oligo1ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTTTGGCTGACT   1 Duplex_p7_oligo1GTCAGCCAAAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC   2 Duplex_p5_oligo2ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGGGTACTGACT   3 Duplex_p7_oligo2GTCAGTACCCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC   4 Duplex_p5_oligo3ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTCAATAGTGACT   5 Duplex_p7_oligo3GTCACTATTGAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC   6 Duplex_p5_oligo4ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGCGCAATGACT   7 Duplex_p7_oligo4GTCATTGCGCGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC   8 Duplex_p5_oligo5ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGGCTCCATGACT   9 Duplex_p7_oligo5GTCATGGAGCCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  10 Duplex_p5_oligo6ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGTGGAATGACT  11 Duplex_p7_oligo6GTCATTCCACCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  12 Duplex_p5_oligo7ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGCTACATGACT  13 Duplex_p7_oligo7GTCATGTAGCGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  14 Duplex_p5_oligo8ACACTCTTTCCCTACACGACGCTCTTCCGATCTAAGATCGTTGACT  15 Duplex_p7_oligo8GTCAACGATCTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  16 Duplex_p5_oligo9ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGTAAGCTGACT  17 Duplex_p7_oligo9GTCAGCTTACGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  18 Duplex_p5_oligo10ACACTCTTTCCCTACACGACGCTCTTCCGATCTATTACCCATGACT  19 Duplex_p7_oligo10GTCATGGGTAATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  20 Duplex_p5_oligo11ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCGTCCATGACT  21 Duplex_p7_oligo11GTCATGGACGACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  22 Duplex_p5_oligo12ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCAGGCGTTGACT  23 Duplex_p7_oligo12GTCAACGCCTGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  24 Duplex_p5_oligo13ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGTACCTTGACT  25 Duplex_p7_oligo13GTCAAGGTACCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  26 Duplex_p5_oligo14ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCTTCGTGACT  27 Duplex_p7_oligo14GTCACGAAGCTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  28 Duplex_p5_oligo15ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCTGGAACTGACT  29 Duplex_p7_oligo15GTCAGTTCCAGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  30 Duplex_p5_oligo16ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGCGAGAATGACT  31 Duplex_p7_oligo16GTCATTCTCGCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  32 Duplex_p5_oligo17ACACTCTTTCCCTACACGACGCTCTTCCGATCTAACCGATGTGACT  33 Duplex_p7_oligo17GTCACATCGGTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  34 Duplex_p5_oligo18ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTACCCGCTGACT  35 Duplex_p7_oligo18GTCAGCGGGTAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  36 Duplex_p5_oligo19ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTCTCTCGTGACT  37 Duplex_p7_oligo19GTCACGAGAGAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  38 Duplex_p5_oligo20ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTTCCCTTGACT  39 Duplex_p7_oligo20GTCAAGGGAAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  40 Duplex_p5_oligo21ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGCGAGGTGACT  41 Duplex_p7_oligo21GTCACCTCGCACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  42 Duplex_p5_oligo22ACACTCTTTCCCTACACGACGCTCTTCCGATCTGACAGGTTTGACT  43 Duplex_p7_oligo22GTCAAACCTGTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  44 Duplex_p5_oligo23ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGTGACCTGACT  45 Duplex_p7_oligo23GTCAGGTCACCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  46 Duplex_p5_oligo24ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAATCTCGTGACT  47 Duplex_p7_oligo24GTCACGAGATTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  48 Duplex_p5_oligo25ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGATCGAATGACT  49 Duplex_p7_oligo25GTCATTCGATCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  50 Duplex_p5_oligo26ACACTCTTTCCCTACACGACGCTCTTCCGATCTACAGTTGTTGACT  51 Duplex_p7_oligo26GTCAACAACTGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  52 Duplex_p5_oligo27ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCACGAATGACT  53 Duplex_p7_oligo27GTCATTCGTGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  54 Duplex_p5_oligo28ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCAATCCTGACT  55 Duplex_p7_oligo28GTCAGGATTGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  56 Duplex_p5_oligo29ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCCGTTGTGACT  57 Duplex_p7_oligo29GTCACAACGGGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  58 Duplex_p5_oligo30ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCGACCAGTGACT  59 Duplex_p7_oligo30GTCACTGGTCGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  60 Duplex_p5_oligo31ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTTCAATTGACT  61 Duplex_p7_oligo31GTCAATTGAAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  62 Duplex_p5_oligo32ACACTCTTTCCCTACACGACGCTCTTCCGATCTATCCGCAGTGACT  63 Duplex_p7_oligo32GTCACTGCGGATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  64 Duplex_p5_oligo33ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTGGCGTTGACT  65 Duplex_p7_oligo33GTCAACGCCAACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  66 Duplex_p5_oligo34ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGCGTTATGACT  67 Duplex_p7_oligo34GTCATAACGCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  68 Duplex_p5_oligo35ACACTCTTTCCCTACACGACGCTCTTCCGATCTGATGTCATTGACT  69 Duplex_p7_oligo35GTCAATGACATCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  70 Duplex_p5_oligo36ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGTGGCATGACT  71 Duplex_p7_oligo36GTCATGCCACACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  72 Duplex_p5_oligo37ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTAGTAGATGACT  73 Duplex_p7_oligo37GTCATCTACTACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  74 Duplex_p5_oligo38ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTACTTGACT  75 Duplex_p7_oligo38GTCAAGTAAGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  76 Duplex_p5_oligo39ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACCTGTGTGACT  77 Duplex_p7_oligo39GTCACACAGGTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  78 Duplex_p5_oligo40ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCTCGAAGTGACT  79 Duplex_p7_oligo40GTCACTTCGAGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  80 Duplex_p5_oligo41ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTGCCCGTGACT  81 Duplex_p7_oligo41GTCACGGGCAGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  82 Duplex_p5_oligo42ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTACACAATGACT  83 Duplex_p7_oligo42GTCATTGTGTAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  84 Duplex_p5_oligo43ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGCACCTTGACT  85 Duplex_p7_oligo43GTCAAGGTGCTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  86 Duplex_p5_oligo44ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTAATTCTTGACT  87 Duplex_p7_oligo44GTCAAGAATTAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  88 Duplex_p5_oligo45ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAGGACCATGACT  89 Duplex_p7_oligo45GTCATGGTCCTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  90 Duplex_p5_oligo46ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACTTGCCTGACT  91 Duplex_p7_oligo46GTCAGGCAAGTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  92 Duplex_p5_oligo47ACACTCTTTCCCTACACGACGCTCTTCCGATCTAATACGTCTGACT  93 Duplex_p7_oligo47GTCAGACGTATTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  94 Duplex_p5_oligo48ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGATGATCTGACT  95 Duplex_p7_oligo48GTCAGATCATCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  96 Duplex_p5_oligo49ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTACATGTTGACT  97 Duplex_p7_oligo49GTCAACATGTACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC  98 Duplex_p5_oligo50ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCGGGATTGACT  99 Duplex_p7_oligo50GTCAATCCCGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 100 Duplex_p5_oligo51ACACTCTTTCCCTACACGACGCTCTTCCGATCTCATATAGCTGACT 101 Duplex_p7_oligo51GTCAGCTATATGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 102 Duplex_p5_oligo52ACACTCTTTCCCTACACGACGCTCTTCCGATCTAACGCGAATGACT 103 Duplex_p7_oligo52GTCATTCGCGTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 104 Duplex_p5_oligo53ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGGCTGTTGACT 105 Duplex_p7_oligo53GTCAACAGCCATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 106 Duplex_p5_oligo54ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGTTTAGTGACT 107 Duplex_p7_oligo54GTCACTAAACATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 108 Duplex_p5_oligo55ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCAACAGTGACT 109 Duplex_p7_oligo55GTCACTGTTGGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 110 Duplex_p5_oligo56ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCGAGTCTGACT 111 Duplex_p7_oligo56GTCAGACTCGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 112 Duplex_p5_oligo57ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCCCATCTGACT 113 Duplex_p7_oligo57GTCAGATGGGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 114 Duplex_p5_oligo58ACACTCTTTCCCTACACGACGCTCTTCCGATCTATTGGACATGACT 115 Duplex_p7_oligo58GTCATGTCCAATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 116 Duplex_p5_oligo59ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCGTTCTATGACT 117 Duplex_p7_oligo59GTCATAGAACGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 118 Duplex_p5_oligo60ACACTCTTTCCCTACACGACGCTCTTCCGATCTAAGTGTGGTGACT 119 Duplex_p7_oligo60GTCACCACACTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 120 Duplex_p5_oligo61ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCATTTCTGACT 121 Duplex_p7_oligo61GTCAGAAATGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 122 Duplex_p5_oligo62ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCATCACGTGACT 123 Duplex_p7_oligo62GTCACGTGATGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 124 Duplex_p5_oligo63ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTCCCGAATGACT 125 Duplex_p7_oligo63GTCATTCGGGAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 126 Duplex_p5_oligo64ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGACTTCCTGACT 127 Duplex_p7_oligo64GTCAGGAAGTCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 128 Duplex_p5_oligo65ACACTCTTTCCCTACACGACGCTCTTCCGATCTGACGCACTTGACT 129 Duplex_p7_oligo65GTCAAGTGCGTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 130 Duplex_p5_oligo66ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGTATCGTGACT 131 Duplex_p7_oligo66GTCACGATACACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 132 Duplex_p5_oligo67ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAAGACTGTGACT 133 Duplex_p7_oligo67GTCACAGTCTTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 134 Duplex_p5_oligo68ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCGTTCTTGACT 135 Duplex_p7_oligo68GTCAAGAACGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 136 Duplex_p5_oligo69ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGGCACTGTGACT 137 Duplex_p7_oligo69GTCACAGTGCCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 138 Duplex_p5_oligo70ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACCTGTATGACT 139 Duplex_p7_oligo70GTCATACAGGTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 140 Duplex_p5_oligo71ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGAATCCATGACT 141 Duplex_p7_oligo71GTCATGGATTCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 142 Duplex_p5_oligo72ACACTCTTTCCCTACACGACGCTCTTCCGATCTAATCCATGTGACT 143 Duplex_p7_oligo72GTCACATGGATTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 144 Duplex_p5_oligo73ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAGTCGATTGACT 145 Duplex_p7_oligo73GTCAATCGACTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 146 Duplex_p5_oligo74ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGGATAATGACT 147 Duplex_p7_oligo74GTCATTATCCACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 148 Duplex_p5_oligo75ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCAGCGCCTGACT 149 Duplex_p7_oligo75GTCAGGCGCTGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 150 Duplex_p5_oligo76ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCACTGGCTGACT 151 Duplex_p7_oligo76GTCAGCCAGTGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 152 Duplex_p5_oligo77ACACTCTTTCCCTACACGACGCTCTTCCGATCTCATTATGGTGACT 153 Duplex_p7_oligo77GTCACCATAATGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 154 Duplex_p5_oligo78ACACTCTTTCCCTACACGACGCTCTTCCGATCTACACGACTTGACT 155 Duplex_p7_oligo78GTCAAGTCGTGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 156 Duplex_p5_oligo79ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAATCGCCTGACT 157 Duplex_p7_oligo79GTCAGGCGATTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 158 Duplex_p5_oligo80ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACCGGCTTGACT 159 Duplex_p7_oligo80GTCAAGCCGGTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 160 Duplex_p5_oligo81ACACTCTTTCCCTACACGACGCTCTTCCGATCTACACCTGCTGACT 161 Duplex_p7_oligo81GTCAGCAGGTGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 162 Duplex_p5_oligo82ACACTCTTTCCCTACACGACGCTCTTCCGATCTCATACCGTTGACT 163 Duplex_p7_oligo82GTCAACGGTATGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 164 Duplex_p5_oligo83ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAGACAGTTGACT 165 Duplex_p7_oligo83GTCAACTGTCTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 166 Duplex_p5_oligo84ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGTCGCTATGACT 167 Duplex_p7_oligo84GTCATAGCGACTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 168 Duplex_p5_oligo85ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCTAGGTTGACT 169 Duplex_p7_oligo85GTCAACCTAGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 170 Duplex_p5_oligo86ACACTCTTTCCCTACACGACGCTCTTCCGATCTGATATGAATGACT 171 Duplex_p7_oligo86GTCATTCATATCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 172 Duplex_p5_oligo87ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAAATCACTGACT 173 Duplex_p7_oligo87GTCAGTGATTTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 174 Duplex_p5_oligo88ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCTCATATGACT 175 Duplex_p7_oligo88GTCATATGAGAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 176 Duplex_p5_oligo89ACACTCTTTCCCTACACGACGCTCTTCCGATCTCATGTGCTTGACT 177 Duplex_p7_oligo89GTCAAGCACATGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 178 Duplex_p5_oligo90ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCAATGGTGACT 179 Duplex_p7_oligo90GTCACCATTGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 180 Duplex_p5_oligo91ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCGACTCTTGGACT 181 Duplex_p7_oligo91GTCAAGAGTCGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 182 Duplex_p5_oligo92ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCCACGATGACT 183 Duplex_p7_oligo92GTCATCGTGGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 184 Duplex_p5_oligo93ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCTGAATGTGACT 185 Duplex_p7_oligo93GTCACATTCAGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 186 Duplex_p5_oligo94ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCAGTCTGACT 187 Duplex_p7_oligo94GTCAGACTGCTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 188 Duplex_p5_oligo95ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGAATACCTGACT 189 Duplex_p7_oligo95GTCAGGTATTCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 190 Duplex_p5_oligo96ACACTCTTTCCCTACACGACGCTCTTCCGATCTGATTGTGCTGACT 191 Duplex_p7_oligo96GTCAGCACAATCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 192 Duplex_p5_oligo97ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCCCACTTGACT 193 Duplex_p7_oligo97GTCAAGTGGGAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 194 Duplex_p5_oligo98ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCATGTGATGACT 195 Duplex_p7_oligo98GTCATCACATGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 196 Duplex_p5_oligo99ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGTGATGTGACT 197 Duplex_p7_oligo99GTCACATCACTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 198 Duplex_p5_oligo100ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGATATCATGACT 199 Duplex_p7_oligo100GTCATGATATCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 200 Duplex_p5_oligo101ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCGCCGAATGACT 201 Duplex_p7_oligo101GTCATTCGGCGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 202 Duplex_p5_oligo102ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGGGAGATGACT 203 Duplex_p7_oligo102GTCATCTCCCTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 204 Duplex_p5_oligo103ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGGACAGTGACT 205 Duplex_p7_oligo103GTCACTGTCCGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 206 Duplex_p5_oligo104ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTCCGGATGACT 207 Duplex_p7_oligo104GTCATCCGGAACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 208 Duplex_p5_oligo105ACACTCTTTCCCTACACGACGCTCTTCCGATCTATTCGGTATGACT 209 Duplex_p7_oligo105GTCATACCGAATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 210 Duplex_p5_oligo106ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTACACAGTGACT 211 Duplex_p7_oligo106GTCACTGTGTAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 212 Duplex_p5_oligo107ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCCGCAATGACT 213 Duplex_p7_oligo107GTCATTGCGGACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 214 Duplex_p5_oligo108ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTTCGGATGACT 215 Duplex_p7_oligo108GTCATCCGAAGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 216 Duplex_p5_oligo109ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTGCTATTGACT 217 Duplex_p7_oligo109GTCAATAGCAGTAGATCGGAAGAGCAACGTCTGAACTCCAGTCACAC 218 Duplex_p5_oligo110ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTACATGTGACT 219 Duplex_p7_oligo110GTCACATGTAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 220 Duplex_p5_oligo111ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCAGTATTGACT 221 Duplex_p7_oligo111GTCAATACTGACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 222 Duplex_p5_oligo112ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCTACGGCTGACT 223 Duplex_p7_oligo112GTCAGCCGTAGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 224 Duplex_p5_oligo113ACACTCTTTCCCTACACGACGCTCTTCCGATCTACGTATTCTGACT 225 Duplex_p7_oligo113GTCAGAATACGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 226 Duplex_p5_oligo114ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGATCTATTGACT 227 Duplex_p7_oligo114GTCAATAGATCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 228 Duplex_p5_oligo115ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGTTCCACTGACT 229 Duplex_p7_oligo115GTCAGTGGAACTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 230 Duplex_p5_oligo116ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGATCCGTGACT 231 Duplex_p7_oligo116GTCACGGATCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 232 Duplex_p5_oligo117ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGTTCGCCTGACT 233 Duplex_p7_oligo117GTCAGGCGAACAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 234 Duplex_p5_oligo118ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCACACCTTGACT 235 Duplex_p7_oligo118GTCAAGGTGTGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 236 Duplex_p5_oligo119ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTAAGCCTTGACT 237 Duplex_p7_oligo119GTCAAGGCTTACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 238 Duplex_p5_oligo120ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACCAATCTGACT 239 Duplex_p7_oligo120GTCAGATTGGTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 240 Duplex_p5_oligo121ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGTAGTTTGACT 241 Duplex_p7_oligo121GTCAAACTACCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 242 Duplex_p5_oligo122ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAATATACTGACT 243 Duplex_p7_oligo122GTCAGTATATTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 244 Duplex_p5_oligo123ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGCCGGTTGACT 245 Duplex_p7_oligo123GTCAACCGGCATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 246 Duplex_p5_oligo124ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTAGTTGTGACT 247 Duplex_p7_oligo124GTCACAACTAGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 248 Duplex_p5_oligo125ACACTCTTTCCCTACACGACGCTCTTCCGATCTAATCGTAATGACT 249 Duplex_p7_oligo125GTCATTACGATTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 250 Duplex_p5_oligo126ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCAAGATTTGACT 251 Duplex_p7_oligo126GTCAAATCTTGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 252 Duplex_p5_oligo127ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTAACCACTGACT 253 Duplex_p7_oligo127GTCAGTGGTTACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 254 Duplex_p5_oligo128ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCAAACTCTGACT 255 Duplex_p7_oligo128GTCAGAGTTTGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 256 Duplex_p5_oligo129ACACTCTTTCCCTACACGACGCTCTTCCGATCTGATAGGGCTGACT 257 Duplex_p7_oligo129GTCAGCCCTATCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 258 Duplex_p5_oligo130ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCAGGACATGACT 259 Duplex_p7_oligo130GTCATGTCCTGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 260 Duplex_p5_oligo131ACACTCTTTCCCTACACGACGCTCTTCCGATCTACGATATGTGACT 261 Duplex_p7_oligo131GTCACATATCGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 262 Duplex_p5_oligo132ACACTCTTTCCCTACACGACGCTCTTCCGATCTACGTGGTTTGACT 263 Duplex_p7_oligo132GTCAAACCACGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 264 Duplex_p5_oligo133ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACGATGGTGACT 265 Duplex_p7_oligo133GTCACCATCGTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 266 Duplex_p5_oligo134ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCGTAAGTGACT 267 Duplex_p7_oligo134GTCACTTACGGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 268 Duplex_p5_oligo135ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTTGGAATGACT 269 Duplex_p7_oligo135GTCATTCCAAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 270 Duplex_p5_oligo136ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGTTGAGGTGACT 271 Duplex_p7_oligo136GTCACCTCAACTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 272 Duplex_p5_oligo137ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTAATGCGTGACT 273 Duplex_p7_oligo137GTCACGCATTACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 274 Duplex_p5_oligo138ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGCTAATCTGACT 275 Duplex_p7_oligo138GTCAGATTAGCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 276 Duplex_p5_oligo139ACACTCTTTCCCTACACGACGCTCTTCCGATCTAAGGTCAATGACT 277 Duplex_p7_oligo139GTCATTGACCTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 278 Duplex_p5_oligo140ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAAAGAGTTGACT 279 Duplex_p7_oligo140GTCAACTCTTTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 280 Duplex_p5_oligo141ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGAAACTTGACT 281 Duplex_p7_oligo141GTCAAGTTTCCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 282 Duplex_p5_oligo142ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCTGGTCTTGACT 283 Duplex_p7_oligo142GTCAAGACCAGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 284 Duplex_p5_oligo143ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCGGAGTTGACT 285 Duplex_p7_oligo143GTCAACTCCGGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 286 Duplex_p5_oligo144ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTCCGATTGACT 287 Duplex_p7_oligo144GTCAATCGGAGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 288 Duplex_p5_oligo145ACACTCTTTCCCTACACGACGCTCTTCCGATCTTATGACGTTGACT 289 Duplex_p7_oligo145GTCAACGTCATAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 290 Duplex_p5_oligo146ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCGATCACTGACT 291 Duplex_p7_oligo146GTCAGTGATCGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 292 Duplex_p5_oligo147ACACTCTTTCCCTACACGACGCTCTTCCGATCTACGAGGAGTGACT 293 Duplex_p7_oligo147GTCACTCCTCGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 294 Duplex_p5_oligo148ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTGAGCTCTGACT 295 Duplex_p7_oligo148GTCAGAGCTCAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 296 Duplex_p5_oligo149ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGAGCGGATGACT 297 Duplex_p7_oligo149GTCATCCGCTCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 298 Duplex_p5_oligo150ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTAGTCGCTGACT 299 Duplex_p7_oligo150GTCAGCGACTAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 300 Duplex_p5_oligo151ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACTTTGGTGACT 301 Duplex_p7_oligo151GTCACCAAAGTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 302 Duplex_p5_oligo152ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGATTACATGACT 303 Duplex_p7_oligo152GTCATGTAATCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 304 Duplex_p5_oligo153ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCTATGATGACT 305 Duplex_p7_oligo153GTCATCATAGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 306 Duplex_p5_oligo154ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGAACGTCTGACT 307 Duplex_p7_oligo154GTCAGACGTTCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 308 Duplex_p5_oligo155ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTTCTTGTGACT 309 Duplex_p7_oligo155GTCACAAGAAACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 310 Duplex_p5_oligo156ACACTCTTTCCCTACACGACGCTCTTCCGATCTAAGGGCGCTGACT 311 Duplex_p7_oligo156GTCAGCGCCCTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 312 Duplex_p5_oligo157ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGCTCTTATGACT 313 Duplex_p7_oligo157GTCATAAGAGCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 314 Duplex_p5_oligo158ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCAACCCGTGACT 315 Duplex_p7_oligo158GTCACGGGTTGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 316 Duplex_p5_oligo159ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGGTGTATGACT 317 Duplex_p7_oligo159GTCATACACCGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 318 Duplex_p5_oligo160ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTCCGATCTGACT 319 Duplex_p7_oligo160GTCAGATCGGACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 320 Duplex_p5_oligo161ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCCAACCTGACT 321 Duplex_p7_oligo161GTCAGGTTGGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 322 Duplex_p5_oligo162ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCTTGCGGTGACT 323 Duplex_p7_oligo162GTCACCGCAAGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 324 Duplex_p5_oligo163ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCGGTACTGACT 325 Duplex_p7_oligo163GTCAGTACCGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 326 Duplex_p5_oligo164ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTCTAGAGTGACT 327 Duplex_p7_oligo164GTCACTCTAGAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 328 Duplex_p5_oligo165ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCCTATATGACT 329 Duplex_p7_oligo165GTCATATAGGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 330 Duplex_p5_oligo166ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCACGTGTGACT 331 Duplex_p7_oligo166GTCACACGTGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 332 Duplex_p5_oligo167ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCGTCTCTGACT 333 Duplex_p7_oligo167GTCAGAGACGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 334 Duplex_p5_oligo168ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGCCTCGTGACT 335 Duplex_p7_oligo168GTCACGAGGCATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 336 Duplex_p5_oligo169ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGAGCTATGACT 337 Duplex_p7_oligo169GTCATAGCTCTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 338 Duplex_p5_oligo170ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCAATAGGTGACT 339 Duplex_p7_oligo170GTCACCTATTGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 340 Duplex_p5_oligo171ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTTGCTCTGACT 341 Duplex_p7_oligo171GTCAGAGCAAACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 342 Duplex_p5_oligo172ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTCGGGTTGACT 343 Duplex_p7_oligo172GTCAACCCGAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 344 Duplex_p5_oligo173ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGACTAATTGACT 345 Duplex_p7_oligo173GTCAATTAGTCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 346 Duplex_p5_oligo174ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTAATTGTGACT 347 Duplex_p7_oligo174GTCACAATTACGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 348 Duplex_p5_oligo175ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGAAAGCATGACT 349 Duplex_p7_oligo175GTCATGCTTTCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 350 Duplex_p5_oligo176ACACTCTTTCCCTACACGACGCTCTTCCGATCTAATCACGGTGACT 351 Duplex_p7_oligo176GTCACCGTGATTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 352 Duplex_p5_oligo177ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTGTACATGACT 353 Duplex_p7_oligo177GTCATGTACAGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 354 Duplex_p5_oligo178ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAGGTGGGTGACT 355 Duplex_p7_oligo178GTCACCCACCTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 356 Duplex_p5_oligo179ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTCGCGGTGACT 357 Duplex_p7_oligo179GTCACCGCGAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 358 Duplex_p5_oligo180ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGATGAGTGACT 359 Duplex_p7_oligo180GTCACTCATCATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 360 Duplex_p5_oligo181ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCGAACGTTGACT 361 Duplex_p7_oligo181GTCAACGTTCGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 362 Duplex_p5_oligo182ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGTGGGTTGACT 363 Duplex_p7_oligo182GTCAACCCACTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 364 Duplex_p5_oligo183ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAACCGAATGACT 365 Duplex_p7_oligo183GTCATTCGGTTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 366 Duplex_p5_oligo184ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAGCTAAGTGACT 367 Duplex_p7_oligo184GTCACTTAGCTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 368 Duplex_p5_oligo185ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGTCACATTGACT 369 Duplex_p7_oligo185GTCAATGTGACTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 370 Duplex_p5_oligo186ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAACCTTCTGACT 371 Duplex_p7_oligo186GTCAGAAGGTTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 372 Duplex_p5_oligo187ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGATGCTGTGACT 373 Duplex_p7_oligo187GTCACAGCATCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 374 Duplex_p5_oligo188ACACTCTTTCCCTACACGACGCTCTTCCGATCTATCAGAGCTGACT 375 Duplex_p7_oligo188GTCAGCTCTGATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 376 Duplex_p5_oligo189ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGAGTGCTTGACT 377 Duplex_p7_oligo189GTCAAGCACTCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 378 Duplex_p5_oligo190ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGTGTGAGTGACT 379 Duplex_p7_oligo190GTCACTCACACCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 380 Duplex_p5_oligo191ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGCCAAGTGACT 381 Duplex_p7_oligo191GTCACTTGGCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 382 Duplex_p5_oligo192ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGACGATGACT 383 Duplex_p7_oligo192GTCATCGTGCACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 384 Duplex_p5_oligo193ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGAGGCGTGACT 385 Duplex_p7_oligo193GTCACGCCTCTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 386 Duplex_p5_oligo194ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCCAAGGTGACT 387 Duplex_p7_oligo194GTCACCTTGGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 388 Duplex_p5_oligo195ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCTCACTTGACT 389 Duplex_p7_oligo195GTCAAGTGAGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 390 Duplex_p5_oligo196ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCCAGTATGACT 391 Duplex_p7_oligo196GTCATACTGGGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 392 Duplex_p5_oligo197ACACTCTTTCCCTACACGACGCTCTTCCGATCTGACGGCTATGACT 393 Duplex_p7_oligo197GTCATAGCCGTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 394 Duplex_p5_oligo198ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTGTAGTTGACT 395 Duplex_p7_oligo198GTCAACTACACGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 396 Duplex_p5_oligo199ACACTCTTTCCCTACACGACGCTCTTCCGATCTAAGCGTACTGACT 397 Duplex_p7_oligo199GTCAGTACGCTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 398 Duplex_p5_oligo200ACACTCTTTCCCTACACGACGCTCTTCCGATCTATTATCGTTGACT 399 Duplex_p7_oligo200GTCAACGATAATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 400 Duplex_p5_oligo201ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCGCATGATGACT 401 Duplex_p7_oligo201GTCATCATGCGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 402 Duplex_p5_oligo202ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACTAGACTGACT 403 Duplex_p7_oligo202GTCAGTCTAGTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 404 Duplex_p5_oligo203ACACTCTTTCCCTACACGACGCTCTTCCGATCTAACGTCCTTGACT 405 Duplex_p7_oligo203GTCAAGGACGTTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 406 Duplex_p5_oligo204ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTGACGATGACT 407 Duplex_p7_oligo204GTCATCGTCAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 408 Duplex_p5_oligo205ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTCTTGGTGACT 409 Duplex_p7_oligo205GTCACCAAGAACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 410 Duplex_p5_oligo206ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAAGCTAGTGACT 411 Duplex_p7_oligo206GTCACTAGCTTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 412 Duplex_p5_oligo207ACACTCTTTCCCTACACGACGCTCTTCCGATCTCATAAGGGTGACT 413 Duplex_p7_oligo207GTCACCCTTATGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 414 Duplex_p5_oligo208ACACTCTTTCCCTACACGACGCTCTTCCGATCTTATGGCCATGACT 415 Duplex_p7_oligo208GTCATGGCCATAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 416 Duplex_p5_oligo209ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCGCTACGTGACT 417 Duplex_p7_oligo209GTCACGTAGCGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 418 Duplex_p5_oligo210ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTAGTACGTGACT 419 Duplex_p7_oligo210GTCACGTACTAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 420 Duplex_p5_oligo211ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCTTGTTTTGACT 421 Duplex_p7_oligo211GTCAAAACAAGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 422 Duplex_p5_oligo212ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGAGTGGTTGACT 423 Duplex_p7_oligo212GTCAACCACTCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 424 Duplex_p5_oligo213ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAACCGAGTGACT 425 Duplex_p7_oligo213GTCACTCGGTTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 426 Duplex_p5_oligo214ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGACAGTGTGACT 427 Duplex_p7_oligo214GTCACACTGTCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 428 Duplex_p5_oligo215ACACTCTTTCCCTACACGACGCTCTTCCGATCTATAGAGGCTGACT 429 Duplex_p7_oligo215GTCAGCCTCTATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 430 Duplex_p5_oligo216ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGACGTTTGACT 431 Duplex_p7_oligo216GTCAAACGTCACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 432 Duplex_p5_oligo217ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGTCTCCTTGACT 433 Duplex_p7_oligo217GTCAAGGAGACCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 434 Duplex_p5_oligo218ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGAAGAAGTGACT 435 Duplex_p7_oligo218GTCACTTCTTCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 436 Duplex_p5_oligo219ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGAACAATGACT 437 Duplex_p7_oligo219GTCATTGTTCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 438 Duplex_p5_oligo220ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGAGGACTTGACT 439 Duplex_p7_oligo220GTCAAGTCCTCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 440 Duplex_p5_oligo221ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGACCAAGTGACT 441 Duplex_p7_oligo221GTCACTTGGTCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 442 Duplex_p5_oligo222ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTGCTTCTGACT 443 Duplex_p7_oligo222GTCAGAAGCAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 444 Duplex_p5_oligo223ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTATGCATGACT 445 Duplex_p7_oligo223GTCATGCATAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 446 Duplex_p5_oligo224ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGTCAATTGACT 447 Duplex_p7_oligo224GTCAATTGACCGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 448 Duplex_p5_oligo225ACACTCTTTCCCTACACGACGCTCTTCCGATCTACACCCTATGACT 449 Duplex_p7_oligo225GTCATAGGGTGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 450 Duplex_p5_oligo226ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGATTACGTGACT 451 Duplex_p7_oligo226GTCACGTAATCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 452 Duplex_p5_oligo227ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGTTGACGTGACT 453 Duplex_p7_oligo227GTCACGTCAACAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 454 Duplex_p5_oligo228ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGTGACTATGACT 455 Duplex_p7_oligo228GTCATAGTCACCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 456 Duplex_p5_oligo229ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCAACTTTGACT 457 Duplex_p7_oligo229GTCAAAGTTGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 458 Duplex_p5_oligo230ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCGTTGATGACT 459 Duplex_p7_oligo230GTCATCAACGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 460 Duplex_p5_oligo231ACACTCTTTCCCTACACGACGCTCTTCCGATCTACAAGGCATGACT 461 Duplex_p7_oligo231GTCATGCCTTGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 462 Duplex_p5_oligo232ACACTCTTTCCCTACACGACGCTCTTCCGATCTTATGGTACTGACT 463 Duplex_p7_oligo232GTCAGTACCATAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 464 Duplex_p5_oligo233ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGCTCTAGTGACT 465 Duplex_p7_oligo233GTCACTAGAGCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 466 Duplex_p5_oligo234ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTGGCTGTGACT 467 Duplex_p7_oligo234GTCACAGCCAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 468 Duplex_p5_oligo235ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCATTAGTGACT 469 Duplex_p7_oligo235GTCACTAATGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 470 Duplex_p5_oligo236ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAATCGGCTGACT 471 Duplex_p7_oligo236GTCAGCCGATTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 472 Duplex_p5_oligo237ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGGTCATCTGACT 473 Duplex_p7_oligo237GTCAGATGACCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 474 Duplex_p5_oligo238ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAGAGATCTGACT 475 Duplex_p7_oligo238GTCAGATCTCTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 476 Duplex_p5_oligo239ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGAGCTCTTGACT 477 Duplex_p7_oligo239GTCAAGAGCTCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 478 Duplex_p5_oligo240ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTAAACTTGACT 479 Duplex_p7_oligo240GTCAAGTTTAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 480 Duplex_p5_oligo241ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCGACGCTGACT 481 Duplex_p7_oligo241GTCAGCGTCGGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 482 Duplex_p5_oligo242ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGACTCAGTGACT 483 Duplex_p7_oligo242GTCACTGAGTCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 484 Duplex_p5_oligo243ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGTAGCATGACT 485 Duplex_p7_oligo243GTCATGCTACTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 486 Duplex_p5_oligo244ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCGCTGTTTGACT 487 Duplex_p7_oligo244GTCAAACAGCGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 488 Duplex_p5_oligo245ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTATAGACTGACT 489 Duplex_p7_oligo245GTCAGTCTATAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 490 Duplex_p5_oligo246ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGCGGCCTTGACT 491 Duplex_p7_oligo246GTCAAGGCCGCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 492 Duplex_p5_oligo247ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCACAACTGACT 493 Duplex_p7_oligo247GTCAGTTGTGAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 494 Duplex_p5_oligo248ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTATAGTCTGACT 495 Duplex_p7_oligo248GTCAGACTATACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 496 Duplex_p5_oligo249ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCATCCGCTGACT 497 Duplex_p7_oligo249GTCAGCGGATGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 498 Duplex_p5_oligo250ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTAATACTGACT 499 Duplex_p7_oligo250GTCAGTATTAGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 500 Duplex_p5_oligo251ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCGTAGAGTGACT 501 Duplex_p7_oligo251GTCACTCTACGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 502 Duplex_p5_oligo252ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCTGATTTGACT 503 Duplex_p7_oligo252GTCAAATCAGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 504 Duplex_p5_oligo253ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGACAACCTGACT 505 Duplex_p7_oligo253GTCAGGTTGTCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 506 Duplex_p5_oligo254ACACTCTTTCCCTACACGACGCTCTTCCGATCTGATAATGTTGACT 507 Duplex_p7_oligo254GTCAACATTATCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 508 Duplex_p5_oligo255ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGGACGGTGACT 509 Duplex_p7_oligo255GTCACCGTCCTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 510 Duplex_p5_oligo256ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAACTAGATGACT 511 Duplex_p7_oligo256GTCATCTAGTTGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 512 Duplex_p5_oligo257ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTCCAACTGACT 513 Duplex_p7_oligo257GTCAGTTGGAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 514 Duplex_p5_oligo258ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGCTGGCTGACT 515 Duplex_p7_oligo258GTCAGCCAGCTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 516 Duplex_p5_oligo259ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTTCCTCCTGACT 517 Duplex_p7_oligo259GTCAGGAGGAACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 518 Duplex_p5_oligo260ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGTGACTGTGACT 519 Duplex_p7_oligo260GTCACAGTCACTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 520 Duplex_p5_oligo261ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCCAGTTTGACT 521 Duplex_p7_oligo261GTCAAACTGGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 522 Duplex_p5_oligo262ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCTCAGCTGACT 523 Duplex_p7_oligo262GTCAGCTGAGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 524 Duplex_p5_oligo263ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGAACACGTGACT 525 Duplex_p7_oligo263GTCACGTGTTCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 526 Duplex_p5_oligo264ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTTTCACTGACT 527 Duplex_p7_oligo264GTCAGTGAAAGGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 528 Duplex_p5_oligo265ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGGTCTCTGACT 529 Duplex_p7_oligo265GTCAGAGACCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 530 Duplex_p5_oligo266ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTGCATTGACT 531 Duplex_p7_oligo266GTCAATGCAGCCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 532 Duplex_p5_oligo267ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCGAAATTGACT 533 Duplex_p7_oligo267GTCAATTTCGAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 534 Duplex_p5_oligo268ACACTCTTTCCCTACACGACGCTCTTCCGATCTAATGCGCGTGACT 535 Duplex_p7_oligo268GTCACGCGCATTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 536 Duplex_p5_oligo269ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTGAACCTGACT 537 Duplex_p7_oligo269GTCAGGTTCAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 538 Duplex_p5_oligo270ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAATACGGTGACT 539 Duplex_p7_oligo270GTCACCGTATTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 540 Duplex_p5_oligo271ACACTCTTTCCCTACACGACGCTCTTCCGATCTGACAATTCTGACT 541 Duplex_p7_oligo271GTCAGAATTGTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 542 Duplex_p5_oligo272ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGTACGGATGACT 543 Duplex_p7_oligo272GTCATCCGTACAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 544 Duplex_p5_oligo273ACACTCTTTCCCTACACGACGCTCTTCCGATCTGCCACGACTGACT 545 Duplex_p7_oligo273GTCAGTCGTGGCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 546 Duplex_p5_oligo274ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCCATCGATGACT 547 Duplex_p7_oligo274GTCATCGATGGAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 548 Duplex_p5_oligo275ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTCTCCATTGACT 549 Duplex_p7_oligo275GTCAATGGAGAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 550 Duplex_p5_oligo276ACACTCTTTCCCTACACGACGCTCTTCCGATCTGTGGCCTATGACT 551 Duplex_p7_oligo276GTCATAGGCCACAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 552 Duplex_p5_oligo277ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTGGGAGTTGACT 553 Duplex_p7_oligo277GTCAACTCCCAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 554 Duplex_p5_oligo278ACACTCTTTCCCTACACGACGCTCTTCCGATCTTTGCTGGATGACT 555 Duplex_p7_oligo278GTCATCCAGCAAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 556 Duplex_p5_oligo279ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTCAAGATGACT 557 Duplex_p7_oligo279GTCATCTTGAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 558 Duplex_p5_oligo280ACACTCTTTCCCTACACGACGCTCTTCCGATCTCATATCCATGACT 559 Duplex_p7_oligo280GTCATGGATATGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 560 Duplex_p5_oligo281ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGACGTTGTGACT 561 Duplex_p7_oligo281GTCACAACGTCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 562 Duplex_p5_oligo282ACACTCTTTCCCTACACGACGCTCTTCCGATCTATTCGAGCTGACT 563 Duplex_p7_oligo282GTCAGCTCGAATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 564 Duplex_p5_oligo283ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTTGCATATGACT 565 Duplex_p7_oligo283GTCATATGCAAGAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 566 Duplex_p5_oligo284ACACTCTTTCCCTACACGACGCTCTTCCGATCTATACAAACTGACT 567 Duplex_p7_oligo284GTCAGTTTGTATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 568 Duplex_p5_oligo285ACACTCTTTCCCTACACGACGCTCTTCCGATCTATGTTCCTTGACT 569 Duplex_p7_oligo285GTCAAGGAACATAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 570 Duplex_p5_oligo286ACACTCTTTCCCTACACGACGCTCTTCCGATCTTGCACAGTTGACT 571 Duplex_p7_oligo286GTCAACTGTGCAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 572 Duplex_p5_oligo287ACACTCTTTCCCTACACGACGCTCTTCCGATCTACATGCACTGACT 573 Duplex_p7_oligo287GTCAGTGCATGTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 574 Duplex_p5_oligo288ACACTCTTTCCCTACACGACGCTCTTCCGATCTTACGGCAGTGACT 575 Duplex_p7_oligo288GTCACTGCCGTAAGATCGGAAGAGCACACGTCTGAACTCCAGTCACAC 576

Example 10 Validation of Sequencing Adapters Containing PredeterminedNon-Randomly Generated Bar Codes

A series of different adapter constructs were tested for librarypreparation efficiency. FIG. 13 compares library preparation efficiencyof duplex adapters in Y-adapter form, including those containing randombase compositions (random duplex Y adapter, or “RDA-Y”) and nonrandombase compositions (nonrandom duplex Y adapter “FDA-Y”), and a nonrandomduplex sequence adapter construct in hairpin-adapter form (nonrandomduplex hairpin adapter, or “FDA-hp”). FDA-Y adapters yielded the bestresults as indicated by the highest library concentration they produced.

The efficiency of FDA-Y adapters was then confirmed and the inputconcentration optimized as shown in FIG. 14 . FIG. 14 shows the ratio oftarget library to adapter dimer ratio at varying input concentrations ofFDA-Y adapters and FDA-hp adapters. Using the ratio of target library,the desired product, to adapter dimers, an undesired product, as aresponse metric allowed optimization of adapter type and inputconcentration to maximize assay efficiency and sensitivity. Again, FDA-Yadapters yielded the best results. In FIG. 14 , the X axis shows thestock concentrations of the adapters and stock solutions of the adapterswere diluted 1:10,000 in ligation reaction. In this experiment, thetarget library used was 25-30 ng cell free DNA. The results alsodemonstrate that FDA-Y adapters, when used in concentrations of 50-600nM in preparing target library, advantageously yielded products havinghigh target library to dimer ratios, i.e., ratios in the range of 9-30.In experiments depicted in FIG. 14 , 100 nM FDA-Y produced the highesttarget library to dimer ratio.

FIG. 15 shows the likelihood of mislabeling, i.e., the undesirableoutcome of two different molecules having the same adapter andfragmentation pattern, thus negating the benefits of including uniquemolecule identifiers in the library preparation process. Because bothcfDNA fragment size and fixed duplex adapter unique molecular index(UMI) motif diversity can be empirically determined, the likelihoodvalue can be calculated as the product of these probability vectors.This probability vector was simulated over a hypothetical 100 kilobase(kb) panel at fixed depth of template diversity (ranging from 500 to2500 fold unique coverage) for 1000 in silico samples at each level. Asfurther proof of concept, FIG. 16 shows the number of incorrectlylabeled molecules, unique templates indistinguishable from each other onthe basis of labeling and fragmentation, tallied for each simulatedsample across the 100 kb panel. On average, less than one mislabeledmolecule was observed for all samples with less than 2500 fold uniquedepth of coverage. This shows that the method is reliable in preparingtarget libraries as described.

Library preparation utilizing FDA-Y adapters is consistentlyreproducible from run to run, as shown in FIG. 17 . A series of sampleswere processed across three consecutive library preparations. Usinglibrary concentration as a response variable, the results wereconsistent from run to run—the CV of all replicates was about 8.1%. Thisresult showed that indicating the process is reproducible over time.

Example 11 Examples of Embodiments

Provided hereafter are non-limiting examples of embodiments.

A1. A method of determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample, comprising:

-   -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein:        -   each of the nonrandom oligonucleotide adapter species            comprises or consists of a first oligonucleotide species and            a second oligonucleotide species; wherein optionally:            -   each of the first oligonucleotide species comprises 5′                to 3′ a polynucleotide A and a polynucleotide B species                and each of the second oligonucleotide species comprises                5′ to 3′ a polynucleotide B′ species and a                polynucleotide A′;            -   each of the polynucleotide B species and the                polynucleotide B′ species are predetermined, are                non-randomly generated, are the same length, and are                about 4 to about 20 consecutive nucleotides in length;            -   there are 999 or fewer polynucleotide B species and each                polynucleotide B′ species is a reverse complement of a                polynucleotide B species;            -   the ratio of nucleic acid templates to polynucleotide B                species is greater than 1,000 to 1;            -   polynucleotide A is not a reverse complement of                polynucleotide A′; and            -   the polynucleotide B species are annealed to                complementary polynucleotide B′ species and                polynucleotide A′ is not annealed to polynucleotide A;                and    -   amplifying the adapter-ligated nucleic acid templates, thereby        generating amplicons; and    -   sequencing all or a portion of each amplicon, thereby        determining a sequence of nucleotides for one or more nucleic        acid templates in the nucleic acid sample.

A.1.1 The method of A1 wherein each of the nonrandom oligonucleotideadapter species comprises or consists of a first oligonucleotide speciesand a second oligonucleotide species, wherein each of the firstoligonucleotide species comprises 5′ to 3′ a polynucleotide A and apolynucleotide B species and each of the second oligonucleotide speciescomprises 5′ to 3′ a polynucleotide B′ species and a polynucleotide A′,and each of the polynucleotide B species and the polynucleotide B′species are predetermined, are non-randomly generated and polynucleotideA is not a reverse complement of polynucleotide A′.

A1.2 The method of A1 wherein each of the polynucleotide B species arethe same length, and are about 4 to about 20 consecutive nucleotides inlength.

A1.3 The method of A1, wherein there are 999 or fewer polynucleotide Bspecies and each polynucleotide B′ species is a reverse complement of apolynucleotide B species.

A1.4 The method of A1, wherein the polynucleotide B species are annealedto complementary polynucleotide B′ species and polynucleotide A′ is notannealed to polynucleotide A

A2. The method of embodiment A1, wherein the polynucleotide B speciesand the polynucleotide B′ species are non-degenerate.

A3. The method of embodiment A1, wherein the polynucleotide B speciesand the polynucleotide B′ species are non-semidegenerate.

A4. The method of any one of embodiments A1-A3, wherein

-   -   each of the first oligonucleotide species comprises a        polynucleotide C species between polynucleotide A and the        polynucleotide B species;    -   each of the second oligonucleotide species comprises a        polynucleotide C′ species between polynucleotide A′ and the        polynucleotide B′ species;    -   each polynucleotide C′ species is the reverse complement of the        polynucleotide C species; and    -   the polynucleotide C species are annealed to complementary        polynucleotide C′ species.

A5. The method of embodiment A4, wherein each of the polynucleotide Cspecies comprises or consists of the same nucleotide sequence.

A6. The method of embodiment A4, wherein the polynucleotide C speciesconsist of at least two different nucleotide sequences.

A7. The method of any one of embodiments A1-A6, wherein thedouble-stranded nucleic acid templates are double-stranded DNAtemplates.

A8. The method of any one of embodiments A1-A6, wherein thedouble-stranded nucleic acid templates are double-stranded RNAtemplates.

A9. The method of any one of embodiments A1-A8, wherein:

-   -   amplifying the adapter-ligated nucleic acid templates generates        double-stranded amplicons, and    -   sequencing comprises sequencing all or a portion of each strand        of the amplicons.

A10. The method of any one of embodiments A1-A8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising linear amplification.

A11. The method of any one of embodiments A1-A8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising exponential amplification.

A12. The method of any one of embodiments A1-A8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising isothermal amplification.

A13 The method of any one of the previous embodiments wherein the firstoligonucleotide and second oligonucleotide are partially matched reversecomplement pairs selected from SEQ ID NOs: 1-576.

A14. The method of any one of embodiments A1-A13, wherein thedouble-stranded nucleic acid templates are blunt-ended.

A15. The method of any one of embodiments A1-A13, wherein the nucleicacid templates comprise at least one blunt end.

A16. The method of any one of embodiments A1-A13, wherein the nucleicacid templates are sheared double-stranded DNA templates.

A17. The method of any one of embodiments A1-A13, wherein the nucleicacid templates are restriction enzyme-digested double-stranded DNAtemplates.

A18. The method of any one of embodiments A1-A17, comprisingblunt-ending the nucleic acid templates before contacting the nucleicacid templates with the nonrandom oligonucleotide adapter species.

A19. The method of any one of embodiments A14 and A18, wherein thenonrandom oligonucleotide adapter species comprise a blunt end.

A20. The method of any one of embodiments A1-A19, wherein thedouble-stranded nucleic acid templates comprise a ligation linker.

A21. The method of any one of embodiments A14 and A15, comprisingjoining a ligation linker to the blunt end of the nucleic acid template.

A22. The method of any one of embodiments A20-A21, wherein the ligationlinker is selected from the group consisting of a A-overhang,T-overhang, a CG-overhang, a blunt end, or any ligatable nucleic acidsequence.

A23. The method of embodiment A22, wherein the ligation linker is anA-overhang.

A24. The method of any one of embodiments A20-A23, wherein thedouble-stranded nonrandom oligonucleotide adapter species comprises aligation linker.

A24.1. The method of embodiment A24 wherein the ligation linker isselected from the group consisting of a A-overhang, T-overhang, aCG-overhang, a blunt end, or any ligatable nucleic acid sequence.

A24.2. The method of embodiment A24, wherein the ligation linker is aT-overhang.

A25. The method of any one of embodiments A1-A24.2, wherein the nucleicacid sample is obtained from a subject.

A26. The method of any one of embodiments A1-A25, wherein the nucleicacid is cell-free nucleic acid.

A27. The method of any one of embodiments A1-A25, wherein the nucleicacid sample is blood plasma, blood serum, or urine.

A28. The method of any one of embodiments A1-A25, wherein the nucleicacid sample is circulating cell-free nucleic acid.

A29. The method of any one of embodiments A1-A25, wherein the nucleicacid sample is isolated from blood plasma, blood serum, or urine.

A30. The method of any one of embodiments A1-A25, wherein the nucleicacid sample is isolated from a sample of tissue, cells, or fluidobtained from a subject.

A31. The method of any one of embodiments A1-A30, wherein the subject ishuman.

A32. The method of any one of embodiments A1-A31, wherein the nucleicacid sample is separated from a sample of tissue, cells, or fluidobtained from a subject.

A33. The method of any one of embodiments A1-A31, wherein the sequenceof nucleotides for the one or more nucleic acid templates in the nucleicacid sample is determined in situ.

A34. The method of any one of embodiments A1-A32, comprising

-   -   capturing a subset of the nucleic acid templates by        hybridization to capture probes under hybridization conditions,        thereby generated captured nucleic acid templates.

A35. The method of any one of embodiments A1-A32, comprising: enrichingfor nucleic acid templates representing one or more selected genes bymeans of amplifying nucleic acid templates in the nucleic acid samplethat are complementary to selected genes.

A36. The method of any one of embodiments A34-A35, wherein the method ofobtaining the nucleic acid sample comprises eluting captured nucleicacid templates from the capture probes.

A37. The method of any one of embodiments A34-A36, wherein the captureprobes are in an array.

A37.1. The method of any one of embodiments A34-A35, wherein the captureprobes are attached to beads.

A37.2. The method of any one of embodiments A1-A37.1, wherein thesequencing depth is at about 500 fold to about 150,000 fold.

A38. The method of any one of embodiments A1-A37.1, wherein thesequencing depth is at about 1,000 fold to about 100,000 fold.

A39. The method of any one of embodiments A1-A37.1, wherein thesequencing depth is at about 10,000 fold to about 70,000 fold.

A40. The method of any one of embodiments A1-A37.1, wherein thesequencing depth is at about 20,000 fold to about 60,000 fold.

A41. The method of any one of embodiments A1-A37.1, wherein thesequencing depth is at about 30,000 fold to about 50,000 fold.

A42. The method of any one of embodiments A1-A41, wherein eachadapter-ligated nucleic acid template comprises or consists of onenonrandom oligonucleotide adapter at a first end and a standardsequencing adapter at a second end.

A43. The method of embodiment A42, wherein there are fewer than 999 Bspecies, and the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is greater than 100,000 to 1.

A44. The method of embodiment A42, wherein there are fewer than 999 Bspecies, and the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is greater than 500,000 to 1.

A45. The method of embodiment A42, wherein there are fewer than 999 Bspecies, and the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is greater than 9,000,000 to 1.

A46. The method of embodiment A42, wherein there are fewer than 999 Bspecies, and the ratio of the number of nucleic acid templates for thenucleic acid sample to the number of polynucleotide B species in thenonrandom oligonucleotide adapters is about 1,000,000 to 1.

A47. The method of any one of embodiments A42-A46, wherein there are 500or fewer polynucleotide B species.

A48. The method of any one of embodiments A42-A46, wherein there are 400or fewer polynucleotide B species.

A49. The method of any one of embodiments A42-A46, wherein there are 300or fewer polynucleotide B species.

A50. The method of any one of embodiments A42-A46, wherein there areabout 200 to about 300 polynucleotide B species.

A51. The method of any one of embodiments A42-A46, wherein there areabout 280 to about 290 polynucleotide B species.

A51.1. The method of any one of embodiments A42-A51, wherein less than90% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

A51.2. The method of any one of embodiments A42-A51, wherein less than50% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

A52. The method of any one of embodiments A42-A51.2, wherein

the presence of a single nucleotide alteration in the nucleic acidtemplate is determined and the single nucleotide alteration is presentat a frequency of 5 percent or lower.

A52.1. The method of any one of embodiments A42-A51.2, wherein

the presence of a single nucleotide alteration in the nucleic acidtemplate is determined and the single nucleotide alteration is presentat a frequency of 0.001 percent or lower.

A53. The method of any one of embodiments A42-A52, comprising providinga base call, wherein each base call represents a single nucleotidelocated at a single nucleotide position in the nucleic acid template.

A54. The method of embodiment A53, wherein the frequency of base callerrors is lower than 1×10⁻³.

A54.1. The method of embodiment A53, wherein the frequency of base callerrors is 0.8×10⁻³ or lower.

A54.2. The method of embodiment A53, wherein the frequency of base callerrors is 0.5×10⁻³ or lower.

A54.3. The method of embodiment A53, wherein the frequency of base callerrors is 1×10⁻⁴ or lower.

A54.4. The method of embodiment A53, wherein the frequency of base callerrors is 0.5×10⁻⁴ or lower.

A54.5. The method of embodiment A53, wherein the frequency of base callerrors is 1×10⁻⁵ or lower.

A55. The method of embodiment A42, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1000 to        1, there are fewer than 300 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A56. The method of embodiment A42, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 10,000 to        1, there are fewer than 300 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A57. The method of embodiment A42, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1000 to        1, there are fewer than 500 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A58. The method of embodiment A42, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1, there are fewer than 300 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A59. The method of embodiment A42, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is lower than 1×10⁻³.

A59.1. The method of embodiment A42, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 0.8×10⁻³ or lower.

A59.2. The method of embodiment A42, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 0.5×10⁻³ or lower.

A59.3. The method of embodiment A42, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 1×10⁻⁴ or lower.

A59.4. The method of embodiment A42, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 0.5×10⁻⁴ or lower.

A59.5. The method of embodiment A42, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 1×10⁻⁵ or lower.

A60. The method of embodiment A42, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are 500 or fewer polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A61. The method of embodiment A42, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are 400 or fewer polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A62. The method of embodiment A42, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are about 200 to about 300 polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A63. The method of embodiment A42, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are 300 or fewer polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A64. The method of embodiment A42, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are about 280 to about 290 polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A64.1. The method of any one of embodiments A42-A64, further comprisingthe steps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) removing from the determination of the sequence nucleic acid        sequences of adapter-ligated nucleic acid templates that        comprise B species or B′ species sequences that are not        identical to a B species or B′ species sequence on the list of        step (a).

A64.2. The method of any one of embodiments A42-A64, further comprisingthe steps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) assigning a weight to the nonrandom oligonucleotide        adapter-ligated nucleic acid template sequences, where the        assigned weight is considered in the determination of at least        one base call.

A64.3. The method of embodiment A64.2, wherein

-   -   (a) nonrandom oligonucleotide adapter-ligated nucleic acid        sequences comprising a B species sequence or a B′ species        sequence that is identical to a B species sequence or a B′        species sequence provided in the list of step (a) are assigned a        weight of 1;    -   (b) nonrandom oligonucleotide adapter-ligated nucleic acid        sequences comprising a B species sequence or a B′ species        sequence that comprises or consists of one nucleotide difference        from a B species sequence or B′ species sequence provided in the        list of step (a) are assigned a weight of 0.5 and    -   (c) nonrandom oligonucleotide adapter-ligated nucleic acid        sequences comprising a B species sequence or a B′ species        sequence that comprise more than one nucleotide difference from        a B species sequence or a B′ species sequence provided in the        list of step (a) are assigned a weight of 0.

A65. The method of any one of embodiments A1-A41, wherein each of theadapter-ligated nucleic acid templates comprises a first nonrandomoligonucleotide adapter at a first end and a second nonrandomoligonucleotide adapter at a second end.

A66. The method of embodiment A65, wherein the B species for at leasttwo adapter-ligated nucleic acid templates comprise or consist of thesame nucleotide sequence.

A67. The method of embodiment A65, wherein the B species for at leasttwo adapter-ligated nucleic acid template comprise or consist of adifferent nucleotide sequence.

A68. The method of embodiment A65, wherein the B′ species at each end ofthe adapter-ligated nucleic templates are the same or different.

A69. The method of any one of embodiments A65-A68, wherein copies of afirst double-stranded adapter species comprising a first B species and afirst B′ species are ligated to at least two double-stranded nucleicacid template species.

A70. The method of any one of embodiments A1-A69, wherein

-   -   copies of a first double-stranded adapter species comprising a        first B species and a first B′ species are ligated to the first        end of the at least two double-stranded nucleic acid templates;        and    -   copies of a second double-stranded adapter species comprising a        second B species and a second B′ species are ligated to the        second end of the at least two double-stranded nucleic acid        templates.

A71. The method of any one of embodiments A69 or A70, wherein the atleast two double-stranded nucleic acid templates comprise nucleotidesequences that differ by at least one nucleotide.

A72. The method of any one of embodiments A69 or A70, wherein the atleast two double-stranded nucleic acid templates consist of nucleotidesequences that differ by at least one nucleotide.

A73. The method of any one of embodiments A65-A72, wherein there arefewer than 999 B species and the ratio of the number of nucleic acidtemplates for the nucleic acid sample to the number of polynucleotide Bspecies in the nonrandom oligonucleotide adapters is greater than100,000 to 1.

A74. The method of any one of embodiments A65-A72, wherein there arefewer than 999 B species and the ratio of the number of nucleic acidtemplates for the nucleic acid sample to the number of polynucleotide Bspecies in the nonrandom oligonucleotide adapters is greater than500,000 to 1.

A75. The method of any one of embodiments A65-A72, wherein there arefewer than 999 B species, and the ratio of the number of nucleic acidtemplates for the nucleic acid sample to the number of polynucleotide Bspecies in the nonrandom oligonucleotide adapters is greater than900,000 to 1.

A76. The method of any one of embodiments A65-A72, wherein there arefewer than 999 B species, and the ratio of the number of nucleic acidtemplates for the nucleic acid sample to the number of polynucleotide Bspecies in the nonrandom oligonucleotide adapters is about 1,000,000 to1.

A77. The method of any one of embodiments A65-A72, wherein there are 500or fewer polynucleotide B species.

A78. The method of any one of embodiments A65-A72, wherein there are 400or fewer polynucleotide B species.

A79. The method of any one of embodiments A65-A72, wherein there are 300or fewer polynucleotide B species.

A80. The method of any one of embodiments A65-A72, wherein there areabout 200 to about 300 polynucleotide B species.

A81. The method of any one of embodiments A65-A72, wherein there areabout 280 to about 290 polynucleotide B species.

A81.1. The method of any one of embodiments A65-A81, wherein less than90% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

A81.2. The method of any one of embodiments A65-A81, wherein less than50% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

A82. The method of any one of embodiments A65-A81.2, wherein thepresence of a single nucleotide alteration in the nucleic acid templateis determined and the single nucleotide alteration is present at afrequency of of 1 percent or lower.

A83. The method of any one of embodiments A65-A81.2, wherein the methodcomprises base calls, wherein each base call represents a singlenucleotide in the nucleic acid template.

A84. The method of embodiment A83, wherein the frequency of base callerrors is lower than 1×10⁻³.

A84.1. The method of embodiment A83, wherein the frequency of base callerrors is 0.8×10⁻³ or lower.

A84.2. The method of embodiment A83, wherein the frequency of base callerrors is 0.5×10⁻³ or lower.

A84.3. The method of embodiment A83, wherein the frequency of base callerrors is 1×10⁻⁴ or lower.

A84.4. The method of embodiment A83, wherein the frequency of base callerrors is 0.5×10⁻⁴ or lower.

A84.5. The method of embodiment A83, wherein the frequency of base callerrors is 1×10⁻⁵ or lower.

A84.6. The method of embodiment A83, wherein the frequency of base callerrors is 0.5×10⁻⁵ or lower.

A84.7. The method of embodiment A83, wherein the frequency of base callerrors is 1×10⁻⁶ or lower.

A84.8. The method of embodiment A83, wherein the frequency of base callerrors is 0.5×10⁻⁶ or lower.

A84.9. The method of embodiment A83, wherein the frequency of base callerrors is 1×10⁻⁷ or lower.

A85. The method of any one of embodiments A65-A84.9, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1000 to        1, there are fewer than 300 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A86. The method of any one of embodiments A65-A84.9, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 10,000 to        1, there are fewer than 300 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A87. The method of any one of embodiments A65-A84.9, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1000 to        1, there are fewer than 500 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A88. The method of any one of embodiments A65-A84.9, wherein

-   -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1, there are fewer than 300 B species, and    -   the presence of a single nucleotide alteration in the nucleic        acid template is determined and the single nucleotide alteration        is present at a frequency of 1 percent or lower.

A89. The method of any one of embodiments A65-A88, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 1×10⁻⁶ or lower.

A89.1. The method of any one of embodiments A65-A88, wherein

-   -   the method comprises base calls, wherein each base call        represents a single nucleotide in the nucleic acid template;    -   the ratio of the number of nucleic acid templates for the        nucleic acid sample to the number of polynucleotide B species in        the nonrandom oligonucleotide adapters is greater than 1,000,000        to 1; and    -   the frequency of base call errors is 1×10⁻⁷ or lower.

A90. The method of embodiment A65, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are 500 or fewer polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A91. The method of embodiment A65, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are 400 or fewer polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A92. The method of embodiment A65, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are about 300 to about 400 polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A93. The method of embodiment A65, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are 300 or fewer polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A94. The method of embodiment A65, wherein

-   -   the presence of a single nucleotide alteration in the nucleic        acid is determined;    -   there are about 280 to about 290 polynucleotide B species; and    -   the single nucleotide alteration is present at a frequency of 1        percent or lower.

A94.1. The method of any one of embodiments A65-A94, further comprisingthe steps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) removing from the determination of the count of nucleic acid        templates, nonrandom oligonucleotide adapter-ligated nucleic        acid templates that comprise B species or B′ species sequences        that are not identical to a B species or B′ species sequence on        the list of step (a).

A94.2. The method of any one of embodiments A65-A94.1, furthercomprising the steps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) assigning a weight to the nonrandom oligonucleotide        adapter-ligated nucleic acid templates, where the assigned        weight is considered in the determination of the count of at        least one nucleic acid template.

A94.3. The method of embodiment A94.2, wherein

-   -   (a) nonrandom oligonucleotide adapter-ligated nucleic acid        templates comprising a B species sequence or a B′ species        sequence that is identical to a B species sequence or a B′        species sequence provided in the list of step (a) are assigned a        weight of 1;    -   (b) nonrandom oligonucleotide adapter-ligated nucleic acid        templates comprising a B species sequence or a B′ species        sequence that comprises or consists of one nucleotide difference        from a B species sequence or B′ species sequence provided in the        list of step (a) are assigned a weight of 0.5 and    -   (c) nonrandom oligonucleotide adapter-ligated nucleic acid        templates comprising a B species sequence or a B′ species        sequence that comprise more than one nucleotide difference from        a B species sequence or a B′ species sequence provided in the        list of step (a) are assigned a weight of 0.

A95. The method of any one of embodiments A1-A94.3 wherein the sequenceof at least one polynucleotide B species and the sequence of at leastone polynucleotide B′ species are determined and are utilized toidentify one or more errors.

A96. The method of embodiment A95, wherein the errors are sequencingerrors and/or amplification errors.

A97. The method of any one of embodiments A1-A96, wherein the sequencinggenerates sequence reads and the sequence reads are mapped to regions ofa reference genome.

A98. The method of any one of embodiments A1-A64, comprising reviewingthe sequence of nucleotides for the one or more nucleic acid templatesin the nucleic acid sample, comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a polynucleotide B species at one end;        and    -   (b) determining the sequence of nucleotides for the one or more        nucleic acid templates in the nucleic acid sample by removing        from the determination of the sequence nucleic acid sequences        having one or more nucleotide positions that disagree with the        nucleotide position determined in 95% of the nucleic acid        sequences of the set of amplicon duplicates.

A99. The method of embodiment A98, comprising the step of selectingamplicons having the same length for the set of amplicon duplicates.

A100. The method of any one of embodiments A65-A97, comprising reviewingthe sequence of nucleotides for the one or more nucleic acid templatesin the nucleic acid sample, comprising

-   -   (a) identifying a first set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a first polynucleotide B species at a        first end and a second polynucleotide B species at the second        end;    -   (b) identifying a second set of amplicon duplicates, wherein the        second set of amplicon duplicates comprise amplified        adapter-ligated nucleic acid templates comprising the B′ species        that are the reverse complement of the first and second B        species of step (a); and    -   (c) determining the sequence of nucleotides for the one or more        nucleic acid templates in the nucleic acid sample by removing        from the determination of the sequence nucleic acid sequences        having one or more nucleotide positions where the first single        strand consensus sequence and the second single strand consensus        sequence disagree at one or more nucleotide positions.

A100.1. The method of embodiment A100, comprising the step of selectingamplicons having the same length for the first and second set ofamplicon duplicates.

A100.2. The method of any one of embodiments A1-A100.1, comprisingdetermining whether a nucleic acid template is ligated to a nonrandomoligonucleotide adapter comprising a polynucleotide B or polynucleotideB′ species, before sequencing the nucleic acid template.

A101. The method of any one of embodiments A1-A100.2, comprisingcounting the nucleic acid templates for the nucleic acid sample,comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a polynucleotide B species; and    -   (b) determining the number of amplicon duplicates comprising the        polynucleotide B species.

A101.1 The method of embodiment A101, comprising comparing the number offirst amplicon duplicates comprising a first polynucleotide B specieswith the number of second amplicon duplicates comprising a secondpolynucleotide B species.

A101.2. The method of embodiment A101.1, wherein the first ampliconduplicates comprise copies of a first nucleic acid template of a firstchromosome and the second amplicon duplicates comprise copies of asecond nucleic acid template of a second chromosome.

A101.3. The method of any one of embodiments A1-A100.2, comprisingcounting the number of unique nucleic acid templates for the nucleicacid sample, comprising

-   -   (a) identifying the nonrandom oligonucleotide adapter species        ligated to each nucleic acid template; and    -   (b) counting the number of nonrandom oligonucleotide adapter        species ligated to the nucleic acid templates for the nucleic        acid sample.

A101.4. The method of any one of embodiments A1-A100.2, comprisingcounting the nucleic acid templates for the nucleic acid sample,comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a first polynucleotide B species and a        second polynucleotide B species at a second end, wherein the        first and second polynucleotide B species may or may not consist        of the same nucleotide sequence; and    -   (b) determining the number of amplicon duplicates comprising        both the first and the second polynucleotide B species.

A101.5 The method of embodiment A101, comprising comparing the number offirst amplicon duplicates comprising the first and second polynucleotideB species with the number of second amplicon duplicates, wherein thesecond amplicon duplicates comprise amplified adapter-ligated nucleicacid templates comprising a third polynucleotide B species and a fourthpolynucleotide species, wherein the third and fourth polynucleotide Bspecies may or may not consist of the same nucleotide sequence.

A101.6. The method of embodiment A101.5, wherein the first ampliconduplicates comprise copies of a first nucleic acid template of a firstchromosome and the second amplicon duplicates comprise copies of asecond nucleic acid template of a second chromosome.

A101.7. The method of any one of embodiments A1-A100.2, comprisingcounting the number of unique nucleic acid templates for the nucleicacid sample, comprising

-   -   (a) identifying the nonrandom oligonucleotide adapter species        pair, wherein the nonrandom oligonucleotide adapter species pair        comprises or consists of the nonrandom oligonucleotide adapter        ligated to the first end of the nucleic acid template and the        nonrandom oligonucleotide adapter ligated to the second end of        the nucleic acid template; and    -   (b) counting the number of nonrandom oligonucleotide adapter        species pairs.

A102. The method of any one of embodiments A1-A100.2, comprisingdetermining a base call of at least one nucleotide of a nucleic acidtemplate, comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a polynucleotide B species at one end;    -   (b) identifying the at least one nucleotide in each amplicon of        the set of amplicon duplicates;    -   (c) determining the base call of the at least one nucleotide        where the identity of the at least one nucleotide is the same in        at least 95% of the amplicons in the set of amplicon duplicates.

A102.1. The method of embodiment A102, comprising counting the nucleicacid templates for the nucleic acid sample that comprise the base callof the at least one nucleotide.

A103. The method of any one of embodiments A1-A100.2, comprising thestep of selecting amplicons having the same length for the set ofamplicon duplicates.

A104. The method of any one of embodiments A65-A97, comprisingdetermining a base call of at least one nucleotide of a nucleic acidtemplate, comprising

-   -   (a) identifying a first set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a first polynucleotide B species and a        second polynucleotide B species;    -   (b) identifying a second set of amplicon duplicates, wherein the        second set of amplicon duplicates comprise amplified        adapter-ligated nucleic acid templates comprising the B′ species        that are the reverse complement of the first and second B        species of step (a);    -   (c) identifying the at least one nucleotide in each amplicon of        the first set, and identifying the at least one nucleotide at        the complementary position in the second set of amplicon        duplicates;    -   (d) determining the base call of the at least one nucleotide        where        -   the identity of the at least one nucleotide is the same in            at least 95% of the amplicons in the first set or the second            set of amplicon duplicates; and        -   the identity of the at least one nucleotide in the first set            of amplicon duplicates is the complement to the identity of            the at least one nucleotide in the complementary position in            the second set of amplicon duplicates.

A105. The method of embodiment A104, comprising the step of selectingamplicons having the same length for the first and second set ofamplicon duplicates.

B1. A method for manufacturing a set of 500 or fewer nonrandom nucleicacid sequencing adapters, for use in determining a sequence ofnucleotides for one or more nucleic acid templates in a nucleic acidsample, wherein the ratio of the nonrandom nucleic acid sequencingadapters to nucleic acid templates of the nucleic acid sample is greaterthan 50 to 1, comprising or consisting essentially of:

-   -   providing first oligonucleotide species and second        oligonucleotide species; wherein optionally:        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a polynucleotide B species and each            of the second oligonucleotide species comprises 5′ to 3′ a            polynucleotide B′ species and a polynucleotide A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 999 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;        -   the ratio of nucleic acid templates to polynucleotide B            species is greater than 1,000 to 1;        -   polynucleotide A is not a reverse complement of            polynucleotide A′; and        -   each of the first oligonucleotide species and each of the            second oligonucleotide species has been synthesized            separately; and    -   in separate pairs contacting each first oligonucleotide species        with each second oligonucleotide species comprising the reverse        complement polynucleotide B′ species under annealing conditions,        thereby generating partially double-stranded adapter species;        wherein the polynucleotide B species are annealed to        complementary polynucleotide B′ species and polynucleotide A′ is        not annealed to polynucleotide A.

B.1.1 The method of B1 wherein each of the nonrandom oligonucleotideadapter species comprises or consists of a first oligonucleotide speciesand a second oligonucleotide species, wherein each of the firstoligonucleotide species comprises 5′ to 3′ a polynucleotide A and apolynucleotide B species and each of the second oligonucleotide speciescomprises 5′ to 3′ a polynucleotide B′ species and a polynucleotide A′,and each of the polynucleotide B species and the polynucleotide B′species are predetermined, are non-randomly generated and polynucleotideA is not a reverse complement of polynucleotide A′.

B1.2 The method of B1 wherein each of the polynucleotide B species arethe same length, and are about 4 to about 20 consecutive nucleotides inlength.

B1.3 The method of B1, wherein there are 999 or fewer polynucleotide Bspecies and each polynucleotide B′ species is a reverse complement of apolynucleotide B species.

B1.4 The method of B1, wherein the polynucleotide B species are annealedto complementary polynucleotide B′ species and polynucleotide A′ is notannealed to polynucleotide A

B2. The method of embodiment B1, wherein the ratio of the nucleic acidsequencing adapters to nucleic acid templates of the nucleic acid sampleis greater than 10,000 to 1,

B3. The method of embodiment B1, wherein the ratio of the nucleic acidsequencing adapters to nucleic acid templates of the nucleic acid sampleis greater than100,000 to 1,

B4. The method of embodiment B1, wherein the ratio of the nucleic acidsequencing adapters to nucleic acid templates of the nucleic acid sampleis greater than 900,000 to 1,

B5. The method of any one of embodiments B1-B4, wherein the set ofnucleic acid sequencing adapters are combined in a vessel.

B6. The method of any one of embodiments B1-B5, wherein thepolynucleotide B species and the polynucleotide B′ species arenon-degenerate polynucleotides.

B7. The method of any one of embodiments B1-B5, wherein thepolynucleotide B species and the polynucleotide B′ species arenon-degenerate non-semidegenerate polynucleotides.

B8. The method of any one of embodiments B1-B7, wherein thepolynucleotide B species and the polynucleotide B′ species are about 6to about 10 consecutive nucleotide bases in length.

B9. The method of embodiment B8, wherein the polynucleotide B speciesand the polynucleotide B′ species are about 8 consecutive nucleotidebases in length.

B10. The method of any one of embodiments B1-B9, wherein there are 400or fewer polynucleotide B species.

B11. The method of any one of embodiments B 1-B9, wherein there are 300or fewer polynucleotide B species.

B12. The method of any one of embodiments B1-B9, wherein there are about200 to about 300 polynucleotide B species.

B13. The method of any one of embodiments B1-B9, wherein there are about280 to about 290 polynucleotide B species.

B14. The method of any one of embodiments B1-B13, wherein eachpolynucleotide B species comprises a nucleotide sequence that differsfrom the other polynucleotide B species in the set by at least twonucleotides.

B15. The method of any one of B1-B14, wherein the first and secondoligonucleotide species are partially matched reverse complement pairsselected from SEQ ID NOs: 1-576.

C1. A method of counting nucleic acid templates for a nucleic acidsample, comprising

-   -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein optionally:        -   each of the nonrandom oligonucleotide adapter species            comprises or consists of a first oligonucleotide species and            a second oligonucleotide species;        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a polynucleotide B species and each            of the second oligonucleotide species comprises 5′ to 3′ a            polynucleotide B′ species and a polynucleotide A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 999 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;        -   the ratio of nucleic acid templates to polynucleotide B            species is greater than 1,000 to 1;        -   polynucleotide A is not a reverse complement of            polynucleotide A′; and        -   the polynucleotide B species are annealed to complementary            polynucleotide B′ species and polynucleotide A′ is not            annealed to polynucleotide A; and    -   amplifying the adapter-ligated nucleic acid templates, thereby        generating amplicons; and    -   identifying a set of amplicon duplicates, wherein the amplicon        duplicates comprise amplified adapter-ligated nucleic acid        templates comprising a polynucleotide B species at one end; and    -   determining the number of amplicon duplicates comprising the        polynucleotide B species.

C.1.1 The method of C1 wherein each of the nonrandom oligonucleotideadapter species comprises or consists of a first oligonucleotide speciesand a second oligonucleotide species, wherein each of the firstoligonucleotide species comprises 5′ to 3′ a polynucleotide A and apolynucleotide B species and each of the second oligonucleotide speciescomprises 5′ to 3′ a polynucleotide B′ species and a polynucleotide A′,and each of the polynucleotide B species and the polynucleotide B′species are predetermined, are non-randomly generated and polynucleotideA is not a reverse complement of polynucleotide A′.

C1.2 The method of C1 wherein each of the polynucleotide B species arethe same length, and are about 4 to about 20 consecutive nucleotides inlength.

C1.3 The method of C1, wherein there are 999 or fewer polynucleotide Bspecies and each polynucleotide B′ species is a reverse complement of apolynucleotide B species.

C1.4 The method of C1, wherein the polynucleotide B species are annealedto complementary polynucleotide B′ species and polynucleotide A′ is notannealed to polynucleotide A

C2. The method of embodiment C1, wherein the polynucleotide B speciesand the polynucleotide B′ species are non-degenerate.

C3. The method of embodiment C1, wherein the polynucleotide B speciesand the polynucleotide B′ species are non-semidegenerate.

C4. The method of any one of embodiments C1-C3, wherein

-   -   each of the first oligonucleotide species comprises a        polynucleotide C species between polynucleotide A and the        polynucleotide B species;    -   each of the second oligonucleotide species comprises a        polynucleotide C′ species between polynucleotide A′ and the        polynucleotide B′ species;    -   each polynucleotide C′ species is the reverse complement of the        polynucleotide C species; and    -   the polynucleotide C species are annealed to complementary        polynucleotide C′ species.

C5. The method of embodiment C4, wherein each of the polynucleotide Cspecies comprises or consists of the same nucleotide sequence.

C6. The method of embodiment C4, wherein the polynucleotide C speciesconsist of at least two different nucleotide sequences.

C7. The method of any one of embodiments C1-C6, wherein thedouble-stranded nucleic acid templates are double-stranded DNAtemplates.

C8. The method of any one of embodiments C1-C6, wherein thedouble-stranded nucleic acid templates are double-stranded RNAtemplates.

C9. The method of any one of C1-C8, wherein the first oligonucleotidespecies and the second oligonucleotide species are partially matchedreverse complement pairs selected from SEQ ID Nos: 1-576.

C10. The method of any one of embodiments C1-C8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising linear amplification.

C11. The method of any one of embodiments C1-C8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising exponential amplification.

C12. The method of any one of embodiments C1-C8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising isothermal amplification.

C13. The method of C1-C12, wherein 288 unique B species are used.

C14. The method of any one of embodiments C1-C12, wherein thedouble-stranded nucleic acid templates are blunt-ended.

C15. The method of any one of embodiments C1-C12, wherein the nucleicacid templates comprise at least one blunt end.

C16. The method of any one of embodiments C1-C12, wherein the nucleicacid templates are sheared double-stranded DNA templates.

C17. The method of any one of embodiments C1-C12, wherein the nucleicacid templates are restriction enzyme-digested double-stranded DNAtemplates.

C18. The method of any one of embodiments C1-C17, comprisingblunt-ending the nucleic acid templates before contacting the nucleicacid templates with the nonrandom oligonucleotide adapter species.

C19. The method of any one of embodiments C14 and C18, wherein thenonrandom oligonucleotide adapter species comprise a blunt end.

C20. The method of any one of embodiments C1-C19, wherein thedouble-stranded nucleic acid templates comprise a ligation linker.

C21. The method of any one of embodiments C14 and C15, comprisingjoining a ligation linker to the blunt end of the nucleic acid template.

C22. The method of any one of embodiments C20-C21, wherein the ligationlinker is selected from the group consisting of a A-overhang,T-overhang, a CG-overhang, a blunt end, or any ligatable nucleic acidsequence.

C23. The method of embodiment C22, wherein the ligation linker is anA-overhang.

C24. The method of any one of embodiments C20-C23, wherein thedouble-stranded nonrandom oligonucleotide adapter species comprises aligation linker.

C24.1. The method of embodiment C24 wherein the ligation linker isselected from the group consisting of a A-overhang, T-overhang, aCG-overhang, a blunt end, or any ligatable nucleic acid sequence.

C24.2. The method of embodiment C24, wherein the ligation linker is aT-overhang.

C25. The method of any one of embodiments C1-C24.2, wherein the nucleicacid sample is obtained from a subject.

C26. The method of any one of embodiments C1-C25, wherein the nucleicacid is cell-free nucleic acid.

C27. The method of any one of embodiments C1-C25, wherein the nucleicacid sample is blood plasma, blood serum, or urine.

C28. The method of any one of embodiments C1-C25, wherein the nucleicacid sample is circulating cell-free nucleic acid.

C29. The method of any one of embodiments C1-C25, wherein the nucleicacid sample is isolated from blood plasma, blood serum, or urine.

C30. The method of any one of embodiments C1-C25, wherein the nucleicacid sample is isolated from a sample of tissue, cells, or fluidobtained from a subject.

C31. The method of any one of embodiments C1-C30, wherein the subject ishuman.

C32. The method of any one of embodiments C1-C31, wherein the nucleicacid sample is separated from a sample of tissue, cells, or fluidobtained from a subject.

C33. The method of any one of embodiments C1-C31, wherein the sequenceof nucleotides for one or more nucleic acid templates in the nucleicacid sample is determined in situ.

C34. The method of any one of embodiments C1-C32, comprising capturing asubset of the nucleic acid templates by hybridization to capture probesunder hybridization conditions, thereby generated captured nucleic acidtemplates.

C35. The method of any one of embodiments C1-C32, comprising: enrichingfor nucleic acid templates representing one or more selected genes bymeans of amplifying nucleic acid templates in the nucleic acid samplethat are complementary to selected genes.

C36. The method of any one of embodiments C34-C35, wherein the method ofobtaining the nucleic acid sample comprises eluting captured nucleicacid templates from the capture probes.

C37. The method of any one of embodiments C34-C36, wherein the captureprobes are in an array.

C37.1. The method of any one of embodiments C34-C35, wherein the captureprobes are attached to beads.

C37.2. The method of any one of embodiments C1-C37.1, wherein thesequencing depth is at about 500 fold to about 150,000 fold.

C38. The method of any one of embodiments C1-C37.1, wherein thesequencing depth is at about 1,000 fold to about 100,000 fold.

C39. The method of any one of embodiments C1-C37.1, wherein thesequencing depth is at about 10,000 fold to about 70,000 fold.

C40. The method of any one of embodiments C1-C37.1, wherein thesequencing depth is at about 20,000 fold to about 60,000 fold.

C41. The method of any one of embodiments C1-C37.1, wherein thesequencing depth is at about 30,000 fold to about 50,000 fold.

C42. The method of any one of embodiments C1-C41, wherein eachadapter-ligated nucleic acid template comprises or consists of onenonrandom oligonucleotide adapter at a first end and a standardsequencing adapter at a second end.

C43. The method of embodiment C42, wherein the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isgreater than 100,000 to 1.

C44. The method of embodiment C42, wherein the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isgreater than 500,000 to 1.

C45. The method of embodiment C42, wherein the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isgreater than 9,000,000 to 1.

C46. The method of embodiment C42, wherein the ratio of the number ofnucleic acid templates for the nucleic acid sample to the number ofpolynucleotide B species in the nonrandom oligonucleotide adapters isabout 1,000,000 to 1.

C47. The method of any one of embodiments C42-C46, wherein there are 500or fewer polynucleotide B species.

C48. The method of any one of embodiments C42-C46, wherein there are 400or fewer polynucleotide B species.

C49. The method of any one of embodiments C42-C46, wherein there are 300or fewer polynucleotide B species.

C50. The method of any one of embodiments C42-C46, wherein there areabout 200 to about 300 polynucleotide B species.

C51. The method of any one of embodiments C42-C46, wherein there areabout 280 to about 290 polynucleotide B species.

C51.1. The method of any one of embodiments C42-C51, wherein less than90% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

C51.2. The method of any one of embodiments C42-C51, wherein less than50% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

C65. The method of any one of embodiments C1-C41, wherein each of theadapter-ligated nucleic acid templates comprises a first nonrandomoligonucleotide adapter at a first end and a second nonrandomoligonucleotide adapter at a second end.

C66. The method of embodiment C65, wherein

-   -   the B species for at least two adapter-ligated nucleic acid        templates consist of the same nucleotide sequence.

C67. The method of embodiment C65, wherein

-   -   the B species for at least two adapter-ligated nucleic acid        template consist of a different nucleotide sequence.

C68. The method of embodiment C65, wherein the B′ species at each end ofthe adapter-ligated nucleic templates are the same or different.

C69. The method of any one of embodiments C65-C68, wherein copies of afirst double-stranded adapter species comprising a first B species and afirst B′ species are ligated to at least two double-stranded nucleicacid templates.

C70. The method of any one of embodiments C1-C69, wherein

-   -   copies of a first double-stranded adapter species comprising a        first B species and a second B′ species are ligated to the first        end of the at least two double-stranded nucleic acid template        species; and    -   copies of a second double-stranded adapter species comprising a        second B species and a second B′ species are ligated to the        second end of the at least two double-stranded nucleic acid        templates.

C71. The method of any one of embodiments C69 or C70, wherein the atleast two double-stranded nucleic acid templates comprise nucleotidesequences that differ by at least one nucleotide.

C72. Reserved.

C73. The method of any one of embodiments C65-C71, wherein the ratio ofthe number of nucleic acid templates for the nucleic acid sample to thenumber of polynucleotide B species in the nonrandom oligonucleotideadapters is greater than 100,000 to 1.

C74. The method of any one of embodiments C65-C71, wherein the ratio ofthe number of nucleic acid templates for the nucleic acid sample to thenumber of polynucleotide B species in the nonrandom oligonucleotideadapters is greater than 500,000 to 1.

C75. The method of any one of embodiments C65-C71, wherein the ratio ofthe number of nucleic acid templates for the nucleic acid sample to thenumber of polynucleotide B species in the nonrandom oligonucleotideadapters is greater than 900,000 to 1.

C76. The method of any one of embodiments C65-C71, wherein the ratio ofthe number of nucleic acid templates for the nucleic acid sample to thenumber of polynucleotide B species in the nonrandom oligonucleotideadapters is about 1,000,000 to 1.

C77. The method of any one of embodiments C65-C71, wherein there are 500or fewer polynucleotide B species.

C78. The method of any one of embodiments C65-C721, wherein there are400 or fewer polynucleotide B species.

C79. The method of any one of embodiments C65-C71, wherein there are 300or fewer polynucleotide B species.

C80. The method of any one of embodiments C65-C71, wherein there areabout 200 to about 300 polynucleotide B species.

C81. The method of any one of embodiments C65-C71, wherein there areabout 280 to about 290 polynucleotide B species.

C81.1. The method of any one of embodiments C65-C81, wherein less than90% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

C81.2. The method of any one of embodiments C65-C81, wherein less than50% of the nonrandom oligonucleotide adapter-ligated nucleic acidtemplates comprise a polynucleotide B species that is different from thepolynucleotide B species on the other nonrandom oligonucleotideadapter-ligated nucleic acid templates.

C82.-C100. Reserved.

C101. The method of any one of embodiments C1-C64, comprising countingthe nucleic acid templates for the nucleic acid sample, comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a polynucleotide B species; and    -   (b) determining the number of amplicon duplicates comprising the        polynucleotide B species.

C101.1 The method of embodiment C101, comprising comparing the number offirst amplicon duplicates comprising a first polynucleotide B specieswith the number of second amplicon duplicates comprising a secondpolynucleotide B species.

C101.2. The method of embodiment C101.1, wherein a ratio of the numberof first amplicon duplicates to the number of second amplicon duplicatesis determined.

C101.3. A method for detecting the presence of a genetic disease ordisorder comprising determining the ratio of the number of firstamplicon duplicates to the number of second amplicon duplicates ofembodiment C101.2.

C101.4. The method of any one of embodiments C101.1 to C101.3, whereinthe first amplicon duplicates are mapped to a first chromosome and thesecond amplicon duplicates are mapped to a second chromosome.

C101.5. The method of any one of embodiments C101 to C101.3, wherein thefirst amplicon duplicates are mapped to a first location on a chromosomeand the second amplicon duplicates are mapped to a second location onthe chromosome.

C101.6. The method of any one of embodiments C101.4 or C101.5, whereinthe ratio of the number of first amplicon duplicates to the number ofsecond amplicon duplicates is compared to the ratio determined from apatient, or patients who have been diagnosed with the genetic disease ordisorder.

C101.7. The method of any one of embodiments C101.4 to C101.6, whereinthe ratio of the number of first amplicon duplicates to the number ofsecond amplicon duplicates is compared to the ratio determined from apatient, or patients who have not been diagnosed with the geneticdisease or disorder.

C101.8. The method of any one of embodiments C1-C81.2, comprisingcounting the number of unique nucleic acid templates for the nucleicacid sample, comprising

-   -   (a) identifying the nonrandom oligonucleotide adapter species        ligated to each nucleic acid template; and    -   (b) counting the number of nonrandom oligonucleotide adapter        species ligated to the nucleic acid templates for the nucleic        acid sample.

C101.9. The method of any one of embodiments C1-C81.2, comprisingcounting the nucleic acid templates for the nucleic acid sample,comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a first polynucleotide B species and a        second polynucleotide B species, wherein the first and second        polynucleotide B species may or may not consist of the same        nucleotide sequence; and    -   (b) determining the number of amplicon duplicates comprising        both the first and the second polynucleotide B species.

C101.10 The method of embodiment C101, comprising comparing the numberof first amplicon duplicates comprising the first and secondpolynucleotide B species with the number of second amplicon duplicates,wherein the second amplicon duplicates comprise amplifiedadapter-ligated nucleic acid templates comprising a third polynucleotideB species and a fourth polynucleotide species, wherein the third andfourth polynucleotide B species may or may not consist of the samenucleotide sequence.

C101.11. The method of embodiment C101.10, wherein the first ampliconduplicates comprise copies of a first nucleic acid template of a firstchromosome and the second amplicon duplicates comprise copies of asecond nucleic acid template of a second chromosome.

C101.12. The method of any one of embodiments C1-C81.2, comprisingcounting the number of unique nucleic acid templates for the nucleicacid sample, comprising

-   -   (a) identifying the nonrandom oligonucleotide adapter species        pair, wherein the nonrandom oligonucleotide adapter species pair        comprises or consists of the nonrandom oligonucleotide adapter        ligated to the nucleic acid template and the nonrandom        oligonucleotide adapter ligated to the the nucleic acid        template; and    -   (b) counting the number of nonrandom oligonucleotide adapter        species pairs.

C102. The method of any one of embodiments C1-C101.12, comprisingdetermining whether a nucleic acid template is ligated to a nonrandomoligonucleotide adapter comprising a polynucleotide B or polynucleotideB′ species, before counting the nucleic acid template.

C103. The method of any one of embodiments C1-C102, further comprisingthe steps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) removing from the determination of the number of nucleic        acid templates, adapter-ligated nucleic acid templates that        comprise B species or B′ species sequences that are not        identical to a B species or B′ species sequence on the list of        step (a).

C104. The method of any one of embodiments C1-C102, further comprisingthe steps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) assigning a weight to the nonrandom oligonucleotide        adapter-ligated nucleic acid template sequences, where the        assigned weight is considered in the determination of the number        of nucleic acid templates.

C105. The method of embodiment C104, wherein

-   -   (a) nonrandom oligonucleotide adapter-ligated nucleic acid        sequences comprising a B species sequence or a B′ species        sequence that is identical to a B species sequence or a B′        species sequence provided in the list of step (a) are assigned a        weight of 1;    -   (b) nonrandom oligonucleotide adapter-ligated nucleic acid        sequences comprising a B species sequence or a B′ species        sequence that comprises or consists of one nucleotide difference        from a B species sequence or B′ species sequence provided in the        list of step (a) are assigned a weight of 0.5; and    -   (c) nonrandom oligonucleotide adapter-ligated nucleic acid        sequences comprising a B species sequence or a B′ species        sequence that comprise more than one nucleotide difference from        a B species sequence or a B′ species sequence provided in the        list of step (a) are assigned a weight of 0.

D1. A method for determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample, comprising:

-   -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein:    -   each of the nonrandom oligonucleotide adapter species comprises        or consists of a first oligonucleotide species and a second        oligonucleotide species;    -   each of the first oligonucleotide species comprises 5′ to 3′ a        polynucleotide A and a polynucleotide B species and each of the        second oligonucleotide species comprises 5′ to 3′ a        polynucleotide B′ species and a polynucleotide A′;    -   each of the polynucleotide B species and the polynucleotide B′        species are predetermined, are non-randomly generated, are the        same length, and are about 4 to about 20 consecutive nucleotides        in length;    -   there are 300 or fewer polynucleotide B species and each        polynucleotide B′ species is a reverse complement of a        polynucleotide B species;    -   polynucleotide A is not a reverse complement of polynucleotide        A′;

the ratio of nucleic acid templates to polynucleotide B species isgreater than 1,000 to 1; and

-   -   the polynucleotide B species are annealed to complementary        polynucleotide B′ species and polynucleotide A′ is not annealed        to polynucleotide A; and    -   amplifying the adapter-ligated nucleic acid templates, thereby        generating amplicons; and

sequencing all or a portion of each amplicon, thereby determining asequence of nucleotides for the one or more nucleic acid templates inthe nucleic acid sample.

D2. The method of embodiment D1, wherein the partially double strandednonrandom oligonucleotide adapter species is a Y adapter.

D3. The method of embodiment D1, wherein the partially double strandednonrandom oligonucleotide adapter species is a hairpin adapter. D4. Themethod of any one of embodiments D1-D3, wherein the polynucleotide Bspecies and the polynucleotide B′ species are non-degenerate.

D5. The method of any one of embodiments D1-D4, wherein

-   -   each of the first oligonucleotide species comprises a        polynucleotide C species between polynucleotide A and the        polynucleotide B species;    -   each of the second oligonucleotide species comprises a        polynucleotide C′ species between polynucleotide A′ and the        polynucleotide B′ species;    -   each polynucleotide C′ species is the reverse complement of the        polynucleotide C species; and    -   the polynucleotide C species are annealed to complementary        polynucleotide C′ species.

D6. The method of embodiment D5, wherein each of the polynucleotide Cspecies comprises or consists of the same nucleotide sequence or whereinthe polynucleotide C species consist of at least two differentnucleotide sequences.

D7. The method of embodiment D1, wherein the double-stranded nucleicacid templates are double-stranded DNA templates or RNA templates.

D8. The method of any one of embodiments D1-D7, wherein:

-   -   amplifying the adapter-ligated nucleic acid templates generates        double-stranded amplicons, and    -   sequencing comprises sequencing all or a portion of each strand        of the amplicons.

D9. The method of any one of embodiments D1-D8, wherein theadapter-ligated nucleic acid templates are amplified by a processcomprising linear amplification, exponential amplification, orisothermal amplification.

D10. The method of any one of embodiments D1-D9, wherein eachadapter-ligated nucleic acid template comprises or consists of onenonrandom oligonucleotide adapter at a first end and a standardsequencing adapter at a second end.

D11. The method of any of embodiments D1 and D7-D10, wherein thedouble-stranded nucleic acid templates are blunt-ended.

D12. The method of any of embodiments D1 and D7-D10, wherein the nucleicacid templates comprise at least one blunt end.

D13. The method of any of embodiments D1 and D7-D10, wherein the nucleicacid templates are sheared double-stranded DNA templates.

D14. The method of any of embodiments D1-D13, comprising blunt-endingthe nucleic acid templates before contacting the nucleic acid templateswith the nonrandom oligonucleotide adapter species.

D15. The method of any of embodiments D1-D14, wherein the nonrandomoligonucleotide adapter species comprise a blunt end.

D16. The method of any of embodiments D1-D14, wherein thedouble-stranded nucleic acid templates comprise a ligation linker.

D17. The method of any of embodiments D1-D15, wherein thedouble-stranded nonrandom oligonucleotide adapter species comprises aligation linker.

D18. The method of embodiment D16 or D17, wherein the ligation linker isselected from the group consisting of a A-overhang, T-overhang, aCG-overhang, a blunt end, or any ligatable nucleic acid sequence.

D19. The method of any of embodiments D1-D18, wherein the presence of asingle nucleotide alteration in the nucleic acid template is determinedand the single nucleotide alteration is present at a frequency of 5percent or lower.

D20. The method of any of embodiments D1-D19, comprising providing abase call, wherein each base call represents a single nucleotide locatedat a single nucleotide position in the nucleic acid template.

D21. The method of any one of embodiments D1-D20, wherein the nucleicacid sample is isolated from blood plasma, blood serum, or urine.

D22. The method of any one of embodiments D1-D20, wherein the nucleicacid sample is cell-free DNA.

D23. The method of any one of embodiments D1-D20, wherein the nucleicacid sample is isolated from a sample of tissue, cells, or fluidobtained from a subject.

D24. The method of embodiment D1, wherein the sequence of thenucleotides for the one or more nucleic acid templates in the nucleicacid sample is determined in situ.

D25. The method of embodiment D1 and D24, wherein the sequencing depthis at about 500 fold to about 150,000 fold.

D26. The method of any of embodiments D1-D25, wherein each of theadapter-ligated nucleic acid templates comprises a first nonrandomoligonucleotide adapter at a first end and a second nonrandomoligonucleotide adapter at a second end.

D27. The method of any of embodiments D1-D26, further comprising thesteps of

-   -   (e) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (f) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (g) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (h) removing from the determination of the count of nucleic acid        templates, nonrandom oligonucleotide adapter-ligated nucleic        acid templates that comprise B species or B′ species sequences        that are not identical to a B species or B′ species sequence on        the list of step (a).

D28. The method of any of embodiments D1-D26, further comprising thesteps of

-   -   (a) obtaining a list of B species and B′ species of the        nonrandom oligonucleotide adapters provided for ligation with        the nucleic acid templates;    -   (b) determining the sequence of the B species or B′ species of        the nonrandom oligonucleotide adapter-ligated nucleic acid        templates;    -   (c) comparing the sequence of the B species or B′ species of        step (b) to the sequences of the B and B′ species on the list of        step (a); and    -   (d) assigning a weight to the nonrandom oligonucleotide        adapter-ligated nucleic acid template sequences, where the        assigned weight is considered in the determination of the number        of nucleic acid templates.

D29. The method of any one of embodiments D1-D28, comprising countingthe number of unique nucleic acid templates for the nucleic acid sample,comprising

-   -   (c) identifying the nonrandom oligonucleotide adapter species        ligated to each nucleic acid template; and    -   (d) counting the number of nonrandom oligonucleotide adapter        species ligated to the nucleic acid templates for the nucleic        acid sample.

D30. The method of any one of embodiments D1-D29, comprising countingthe nucleic acid templates for the nucleic acid sample, comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a first polynucleotide B species and a        second polynucleotide B species at a second end, wherein the        first and second polynucleotide B species may or may not consist        of the same nucleotide sequence; and    -   (b) determining the number of amplicon duplicates comprising        both the first and the second polynucleotide B species.

D31. The method of any one of embodiments D1-D30, comprising determininga base call of at least one nucleotide of a nucleic acid template,comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a polynucleotide B species at one end;    -   (b) identifying the at least one nucleotide in each amplicon of        the set of amplicon duplicates;    -   (c) determining the base call of the at least one nucleotide        where the identity of the at least one nucleotide is the same in        at least 95% of the amplicons in the set of amplicon duplicates.

D32. A method for manufacturing a set of 500 or fewer nonrandom nucleicacid sequencing adapters, for use in determining a sequence ofnucleotides for one or more nucleic acid templates in a nucleic acidsample, wherein the ratio of the nonrandom nucleic acid sequencingadapters to nucleic acid templates of the nucleic acid sample is greaterthan 50 to 1, consisting essentially of:

-   -   providing first oligonucleotide species and second        oligonucleotide species; wherein:        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a polynucleotide B species and each            of the second oligonucleotide species comprises 5′ to 3′ a            polynucleotide B′ species and a polynucleotide A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 999 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;        -   the ratio of nucleic acid templates to polynucleotide B            species is greater than 1,000 to 1;        -   polynucleotide A is not a reverse complement of            polynucleotide A′; and        -   each of the first oligonucleotide species and each of the            second oligonucleotide species has been synthesized            separately; and

in separate pairs contacting each first oligonucleotide species witheach second oligonucleotide species comprising the reverse complementpolynucleotide B′ species under annealing conditions, thereby generatingpartially double-stranded adapter species; wherein the polynucleotide Bspecies are annealed to complementary polynucleotide B′ species andpolynucleotide A′ is not annealed to polynucleotide A.

D33. The method of embodiment D32, wherein the polynucleotide B speciesand the polynucleotide B′ species are non-degenerate polynucleotides.

D34. A method of counting nucleic acid templates for a nucleic acidsample, comprising

-   -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein:        -   each of the nonrandom oligonucleotide adapter species            comprises or consists of a first oligonucleotide species and            a second oligonucleotide species;        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a polynucleotide B species and each            of the second oligonucleotide species comprises 5′ to 3′ a            polynucleotide B′ species and a polynucleotide A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 999 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;        -   the ratio of nucleic acid templates to polynucleotide B            species is greater than 1,000 to 1;        -   polynucleotide A is not a reverse complement of            polynucleotide A′; and        -   the polynucleotide B species are annealed to complementary            polynucleotide B′ species and polynucleotide A′ is not            annealed to polynucleotide A; and    -   amplifying the adapter-ligated nucleic acid templates, thereby        generating amplicons; and    -   identifying a set of amplicon duplicates, wherein the amplicon        duplicates comprise amplified adapter-ligated nucleic acid        templates comprising a polynucleotide B species at one end; and    -   determining the number of amplicon duplicates comprising the        polynucleotide B species.

D35. The method of embodiment D34, wherein the polynucleotide B speciesand the polynucleotide B′ species are non-degenerate.

D36. The method of embodiment D34 or D35, wherein

-   -   each of the first oligonucleotide species comprises a        polynucleotide C species between polynucleotide A and the        polynucleotide B species;    -   each of the second oligonucleotide species comprises a        polynucleotide C′ species between polynucleotide A′ and the        polynucleotide B′ species;    -   each polynucleotide C′ species is the reverse complement of the        polynucleotide C species; and    -   the polynucleotide C species are annealed to complementary        polynucleotide C′ species.

D37. The method of embodiment D36, wherein each of the polynucleotide Cspecies comprises or consists of the same nucleotide sequence or whereinthe polynucleotide C species consist of at least two differentnucleotide sequences.

D38. The method of embodiment D34, wherein the double-stranded nucleicacid templates are double-stranded DNA templates or RNA templates.

D39. The method of embodiments D34 or D38, wherein the double-strandednucleic acid templates comprise a ligation linker.

D40. The method of embodiment D34-D37, wherein the double-strandednonrandom oligonucleotide adapter species comprises a ligation linker.

D41. The method of either of embodiments D39 or D40, wherein theligation linker is selected from the group consisting of a A-overhang,T-overhang, a CG-overhang, a blunt end, or any ligatable nucleic acidsequence.

D42. The method of any one of embodiments D34-D41, wherein each of theadapter-ligated nucleic acid templates comprises a first nonrandomoligonucleotide adapter at a first end and a second nonrandomoligonucleotide adapter at a second end.

D43. The method of any one of embodiments D34-D42, comprising countingthe nucleic acid templates for the nucleic acid sample, comprising

-   -   (a) identifying a set of amplicon duplicates, wherein the        amplicon duplicates comprise amplified adapter-ligated nucleic        acid templates comprising a polynucleotide B species; and    -   (b) determining the number of amplicon duplicates comprising the        polynucleotide B species.

D44. The method of embodiment D43, comprising comparing the number offirst amplicon duplicates comprising a first polynucleotide B specieswith the number of second amplicon duplicates comprising a secondpolynucleotide B species.

D45. A method of embodiment D44, comprising detecting the presence of agenetic disease or disorder comprising determining a ratio of the numberof first amplicon duplicates to the number of second ampliconduplicates.

D46. A method for determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample, comprising:

-   -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein:        -   each of the nonrandom oligonucleotide adapter species            comprises or consists of a first oligonucleotide species and            a second oligonucleotide species;        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a polynucleotide B species and each            of the second oligonucleotide species comprises 5′ to 3′ a            polynucleotide B′ species and a polynucleotide A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 300 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;

polynucleotide A is not a reverse complement of polynucleotide A′;

-   -   the ratio of nucleic acid templates to polynucleotide B species        is greater than 1,000,000 to 1; and    -   the polynucleotide B species are annealed to complementary        polynucleotide B′ species and polynucleotide A′ is not annealed        to polynucleotide A; and    -   amplifying the adapter-ligated nucleic acid templates, thereby        generating amplicons; and    -   sequencing all or a portion of each amplicon, thereby        determining a sequence of nucleotides for the one or more        nucleic acid templates in the nucleic acid sample.

D47. The method of any of embodiments D1-D3, wherein generatingadapter-ligated nucleic acid templates is performed by contacting 20-40ng double-stranded nucleic acid templates of the nucleic acid samplewith 50-500 nM partially double-stranded nonrandom oligonucleotideadapter species under ligation conditions.

D48. The method of any one of D1-D47, wherein the first oligonucleotideand the second oligonucleotide are partially matched reverse complementpairs selected from SEQ ID NOs: 1-576.

E1. A composition comprising a plurality of partially double-strandednonrandom oligonucleotide adapter molecules, wherein:

-   -   each of the nonrandom oligonucleotide adapter species comprises        a first oligonucleotide species and a second oligonucleotide        species, wherein each of the first oligonucleotide species        comprises 5′ to 3′ a polynucleotide A and a polynucleotide B        species and each of the second oligonucleotide species comprises        5′ to 3′ a polynucleotide B′ species and a polynucleotide A′,        and wherein each of the polynucleotide B species and the        polynucleotide B′ species are the reverse complement of each        other and each of the polynucleotide A species is not a reverse        complement of polynucleotide A′ species.

E2. The composition of claim E1, wherein the B and B′ species arepredetermined sequences and are non-randomly generated.

E3. The composition of claim E1, wherein the individual pairs of B andB′ species are the same length.

E4. The composition of claim E1, wherein the individual pairs of B andB′ species are the same length but different B and B′ pairs may havedifferent lengths than each other.

E5. The composition of claim E1, wherein the B and B′ species are about4 to about 20 nucleotides in length.

E6. The composition of claim E1, wherein there are 300 or fewerpolynucleotide B species.

E7. The composition of claim E1, wherein the polynucleotide B speciesare annealed to complementary polynucleotide B′ species andpolynucleotide A′ is not annealed to polynucleotide A.

E8. The composition of claim E1, wherein the composition is prepared fora sequencing reaction wherein the ratio of nucleic acid templates topolynucleotide B species is greater than 1,000 to 1.

E9. The composition of any one of E1-E8, The first oligonucleotidespecies and the second oligonucleotide species are partially matchedreverse complement pairs selected from SEQ ID Nos: 1-576.

F1. A system for determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample and/or performing any ofthe methods and/or using any of the compositions of any of the previousembodiments, comprising:

-   -   one or more processors; and memory coupled to one or more        processors, the memory encoded with a set of instructions        configured to perform a process comprising:    -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein:        -   each of the nonrandom oligonucleotide adapter species            comprises a first oligonucleotide species and a second            oligonucleotide species;        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a 5′-3′ polynucleotide B species and            each of the second oligonucleotide species comprises 5′ to            3′ a polynucleotide B′ species and a 5′ to 3′ polynucleotide            A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 300 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;        -   polynucleotide A is not a reverse complement of            polynucleotide A′;        -   the ratio of nucleic acid templates to polynucleotide B            species is greater than 1,000 to 1;        -   the polynucleotide B species anneal to the complementary            polynucleotide B′ species and the polynucleotide A′ species            does not anneal to the polynucleotide A species; amplifying            the adapter-ligated nucleic acid templates, thereby            generating amplicons; and    -   sequencing all or a portion of each amplicon, thereby        determining a sequence of nucleotides for the one or more        nucleic acid templates in the nucleic acid sample.

F2. A non-transitory computer readable storage medium storinginstructions for determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample and/or performing any ofthe methods and/or using any of the compositions of any of the previousembodiments and that, when executed by one or more processors of acomputing system, cause the computing system to execute method stepscomprising:

-   -   contacting double-stranded nucleic acid templates of the nucleic        acid sample with partially double-stranded nonrandom        oligonucleotide adapter species under ligation conditions,        thereby generating adapter-ligated nucleic acid templates,        wherein:        -   each of the nonrandom oligonucleotide adapter species            comprises a first oligonucleotide species and a second            oligonucleotide species;        -   each of the first oligonucleotide species comprises 5′ to 3′            a polynucleotide A and a 5′-3′ polynucleotide B species and            each of the second oligonucleotide species comprises 5′ to            3′ a polynucleotide B′ species and a 5′ to 3′ polynucleotide            A′;        -   each of the polynucleotide B species and the polynucleotide            B′ species are predetermined, are non-randomly generated,            are the same length, and are about 4 to about 20 consecutive            nucleotides in length;        -   there are 300 or fewer polynucleotide B species and each            polynucleotide B′ species is a reverse complement of a            polynucleotide B species;        -   polynucleotide A is not a reverse complement of            polynucleotide A′;        -   the ratio of nucleic acid templates to polynucleotide B            species is greater than 1,000 to 1;        -   the polynucleotide B species anneal to the complementary            polynucleotide B′ species and the polynucleotide A′ species            does not anneal to the polynucleotide A species;    -   amplifying the adapter-ligated nucleic acid templates, thereby        generating amplicons; and    -   sequencing all or a portion of each amplicon, thereby        determining a sequence of nucleotides for the one or more        nucleic acid templates in the nucleic acid sample.

The entirety of each patent, patent application, publication anddocument referenced herein hereby is incorporated by reference. Citationof the above patents, patent applications, publications and documents isnot an admission that any of the foregoing is pertinent prior art, nordoes it constitute any admission as to the contents or date of thesepublications or documents. Their citation is not an indication of asearch for relevant disclosures. All statements regarding the date(s) orcontents of the documents is based on available information and is notan admission as to their accuracy or correctness.

Modifications may be made to the foregoing without departing from thebasic aspects of the technology. Although the technology has beendescribed in substantial detail with reference to one or more specificembodiments, those of ordinary skill in the art will recognize thatchanges may be made to the embodiments specifically disclosed in thisapplication, yet these modifications and improvements are within thescope and spirit of the technology.

The technology illustratively described herein suitably may be practicedin the absence of any element(s) not specifically disclosed herein.Thus, for example, in each instance herein any of the terms“comprising,” “consisting essentially of,” and “consisting of” may bereplaced with either of the other two terms. The terms and expressionswhich have been employed are used as terms of description and not oflimitation, and use of such terms and expressions do not exclude anyequivalents of the features shown and described or portions thereof, andvarious modifications are possible within the scope of the technologyclaimed. The terms “method” and “process” are used interchangeablyherein. The term “a” or “an” can refer to one of or a plurality of theelements it modifies (e.g., “a reagent” can mean one or more reagents)unless it is contextually clear either one of the elements or more thanone of the elements is described. The term “about” as used herein refersto a value within 10% of the underlying parameter (i.e., plus or minus10%), and use of the term “about” at the beginning of a string of valuesmodifies each of the values (i.e., “about 1, 2 and 3” refers to about 1,about 2 and about 3). For example, a weight of “about 100 grams” caninclude weights between 90 grams and 110 grams. Further, when a listingof values is described herein (e.g., about 50%, 60%, 70%, 80%, 85% or86%) the listing includes all intermediate and fractional values thereof(e.g., 54%, 85.4%). Thus, it should be understood that although thepresent technology has been specifically disclosed by representativeembodiments and optional features, modification and variation of theconcepts herein disclosed may be resorted to by those skilled in theart, and such modifications and variations are considered within thescope of this technology.

Certain embodiments of the technology are set forth in the claim(s) thatfollow(s).

1. A method for determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample, comprising: contactingdouble-stranded nucleic acid templates of the nucleic acid sample withpartially double-stranded nonrandom oligonucleotide adapter speciesunder ligation conditions, thereby generating adapter-ligated nucleicacid templates, wherein: each of the nonrandom oligonucleotide adapterspecies comprises a first oligonucleotide species and a secondoligonucleotide species; each of the first oligonucleotide speciescomprises 5′ to 3′ a polynucleotide A and a 5′-3′ polynucleotide Bspecies and each of the second oligonucleotide species comprises 5′ to3′ a polynucleotide B′ species and a 5′ to 3′ polynucleotide A′; each ofthe polynucleotide B species and the polynucleotide B′ species arepredetermined, are non-randomly generated, are the same length, and areabout 4 to about 20 consecutive nucleotides in length; there are 300 orfewer polynucleotide B species and each polynucleotide B′ species is areverse complement of a polynucleotide B species; polynucleotide A isnot a reverse complement of polynucleotide A′; the ratio of nucleic acidtemplates to polynucleotide B species is greater than 1,000 to 1; thepolynucleotide B species anneal to the complementary polynucleotide B′species and the polynucleotide A′ species does not anneal to thepolynucleotide A species; amplifying the adapter-ligated nucleic acidtemplates, thereby generating amplicons; and sequencing all or a portionof each amplicon, thereby determining a sequence of nucleotides for theone or more nucleic acid templates in the nucleic acid sample. 2-45.(canceled)
 46. A composition comprising a plurality of partiallydouble-stranded nonrandom oligonucleotide adapter molecules, whereineach of the nonrandom oligonucleotide adapter species comprises: a firstoligonucleotide species and a second oligonucleotide species, whereineach of the first oligonucleotide species comprises 5′ to 3′ apolynucleotide A and a 5′ to 3′ polynucleotide B species; each of thesecond oligonucleotide species comprises 5′ to 3′ a polynucleotide B′species and a 5′ to 3′ polynucleotide A′ species; and wherein each ofthe polynucleotide B species and the polynucleotide B′ species are thereverse complement of each other and each of the polynucleotide Aspecies is not a reverse complement of polynucleotide A′ species, eachof the nonrandom oligonucleotide adapter species comprises a firstoligonucleotide species and a second oligonucleotide species; each ofthe first oligonucleotide species comprises a polynucleotide A speciesand a polynucleotide B species and each of the second oligonucleotidespecies comprises a polynucleotide B′ species and a polynucleotide A′species; each of the polynucleotide B species and the polynucleotide B′species are predetermined, are non-randomly generated and are about 4 toabout 20 consecutive nucleotides in length; there are 999 or fewerpolynucleotide B species and each polynucleotide B′ species is a reversecomplement of a polynucleotide B species; each polynucleotide A speciesis not a reverse complement of polynucleotide A′ species; the ratio ofthe double-stranded nucleic acid template species to the polynucleotideB species is greater than 1,000 to 1; the polynucleotide B speciesanneal to the complementary polynucleotide B′ species and thepolynucleotide A′ species does not anneal to the polynucleotide Aspecies.
 47. The composition of claim 46, wherein the polynucleotide Bspecies and the polynucleotide B′ species are predetermined sequencesand are non-randomly generated.
 48. The composition of claim 46, whereinthe individual pairs of polynucleotide B species and the polynucleotideB′ species are the same length.
 49. The composition of claim 46 whereinthe individual pairs of the polynucleotide B species and thepolynucleotide B′ species are the same length but differentpolynucleotide B species and polynucleotide B′ species pairs may havedifferent lengths than each other.
 50. The composition of claim 46,wherein each of the polynucleotide B species and the polynucleotide B′species are about 4 to about 20 nucleotides in length.
 51. Thecomposition of claim 46, wherein there are 300 or fewer polynucleotide Bspecies.
 52. The composition of claim 46, wherein the polynucleotide Bspecies are annealed to the complementary polynucleotide B′ species andthe polynucleotide A′ species does not anneal to the polynucleotide Aspecies. 53 (Original) The composition of claim 46, wherein thecomposition is prepared for a sequencing reaction wherein the ratio ofnucleic acid templates to polynucleotide B species is greater than 1,000to
 1. 54. The composition of claim 46, wherein the first oligonucleotideand the second oligonucleotide are partially matched reverse complementpairs selected from SEQ ID NOs: 1-576.
 55. (canceled)
 56. A system fordetermining a sequence of nucleotides for one or more nucleic acidtemplates in a nucleic acid sample, comprising: one or more processors;and memory coupled to one or more processors, the memory encoded with aset of instructions configured to perform a process comprising themethod steps of claim
 1. 57. (canceled)
 58. The composition of claim 46,wherein the partially double-stranded nonrandom oligonucleotide adapterspecies is a Y adapter or a hairpin adapter.
 59. The composition ofclaim 46, wherein the polynucleotide B species and the polynucleotide B′species are non-degenerate.
 60. The composition of claim 46, whereineach of the first oligonucleotide species comprises a polynucleotide Cspecies between polynucleotide A and the polynucleotide B species; eachof the second oligonucleotide species comprises a polynucleotide C′species between polynucleotide A′ and the polynucleotide B′ species;each polynucleotide C′ species is the reverse complement of thepolynucleotide C species; and the polynucleotide C species anneal tocomplementary polynucleotide C′ species.
 61. The composition of claim46, wherein each of the polynucleotide C species comprises the samenucleotide sequence or wherein the polynucleotide C species comprises atleast two different nucleotide sequences.
 62. The composition of claim46, wherein the nonrandom oligonucleotide adapter species comprise ablunt end.
 63. The composition of claim 46, wherein the double-strandednonrandom oligonucleotide adapter species comprises a ligation linker.64. A kit comprising the composition of claim
 46. 65. The kit of claim64, wherein the kit further comprises a list of the sequence informationof B species and B′ species of the nonrandom oligonucleotide adapters.65. The kit of claim 64, further comprising reagents for treating thenucleic acid templates to generate blunt-ended nucleic acid templates.66. A method of determining a sequence of nucleotides for one or morenucleic acid templates in a nucleic acid sample, comprising contactingdouble-stranded nucleic acid templates of the nucleic acid sample withpartially double-stranded nonrandom oligonucleotide adapter species inthe composition of claim 46 under ligation conditions, therebygenerating adapter-ligated nucleic acid templates, amplifying theadapter-ligated nucleic acid templates, thereby generating amplicons;and sequencing all or a portion of each amplicon, thereby determining asequence of nucleotides for the one or more nucleic acid templates inthe nucleic acid sample.
 67. A method of counting nucleic acid templatesfor a nucleic acid sample, comprising contacting double-stranded nucleicacid templates of the nucleic acid sample with partially double-strandednonrandom oligonucleotide adapter species in the composition of claim 46under ligation conditions, thereby generating adapter-ligated nucleicacid templates: amplifying the adapter-ligated nucleic acid templates,thereby generating amplicons; identifying a set of amplicon duplicates,wherein the amplicon duplicates comprise amplified adapter-ligatednucleic acid templates comprising a polynucleotide B species at one end;and determining the number of amplicon duplicates comprising thepolynucleotide B species.
 68. The method of claim 66, wherein thedouble-stranded nucleic acid templates are double-stranded DNA templatesor RNA templates.
 69. The method of claim 67, wherein thedouble-stranded nucleic acid templates are double-stranded DNA templatesor RNA templates.
 70. The method of claim 66, wherein thedouble-stranded nucleic acid templates comprise a ligation linker. 71.The method of claim 67, wherein the double-stranded nucleic acidtemplates comprise a ligation linker.
 72. The method of claim 66,wherein the ligation linker comprises at least one of a A-overhang,T-overhang, a CG-overhang, a blunt end, or any ligatable nucleic acidsequence.
 73. The method of claim 67, wherein the ligation linkercomprises at least one of a A-overhang, T-overhang, a CG-overhang, ablunt end, or any nucleic acid sequence.
 74. The method of claim 66,wherein each of the adapter-ligated nucleic acid templates comprises afirst nonrandom oligonucleotide adapter at a first end and a secondnonrandom oligonucleotide adapter at a second end.
 75. The method ofclaim 67, wherein each of the adapter-ligated nucleic acid templatescomprises a first nonrandom oligonucleotide adapter at a first end and asecond nonrandom oligonucleotide adapter at a second end.